dmitri warren de klerk - 2653786 - thesis

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A FACE DETECTION SYSTEM USED FORACCESS CONTROL

by

Dmitri Warren De Klerk

A mini-thesis submitted in partial fulfillment of therequirements for the degree of

Bachelor of Science (Honours) in Computer Science

University of the Western Cape

Supervisor: Mr. J. Connan

November 2009


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University of Western Cape

Abstract

A FACE DETECTION SYSTEM USED FORACCESS CONTROL

by Dmitri Warren De Klerk

Supervisor: Mr. J. ConnanDepartment of Computer Science

In this project we develop a face detection system which is used for access control. Theface detection system will accurately determine the locations and sizes of all possiblehuman faces. The faces are then scaled to a recognizable size and passed to a facerecognition system implemented by Desmond Eustin van Wyk [1]that can accuratelydetermine the identity of a person and decide whether or not to grant them access to afacility.


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TABLE OF CONTENTS

Table of Contents iii

declaration v

keywords vi

Acknowledgments vii

Glossary viii

CHAPTER 1 10

1.1 - Users view of the problem 10

1.2 - Problem domain 11

1.3 - Complete description of the problem 11

1.4 - Expectations from a system 12

CHAPTER 2 13

2.1 - Users requirements interpretation 13

2.2 - Existing solutions 14

2.3 - Suggested system 14

CHAPTER 3 16

3.1Complete user interface 16

3.1.1 - How the user interface behaves 19

3.1.2 - The Add User Dialog 20

3.1.3 - The Confirm User Delete dialog 22

3.1.4 - The File menu 233.1.5 - The Face Detection menu 24

3.1.6Face Detection Settings dialog 25

3.1.7The Help menu 26

3.1.8The User Guide dialog 27

3.1.9The About dialog 28

CHAPTER 4 30

Object Orientated Analysis (OOA) 30

4.1 - Data dictionary 30

4.2 - Class diagrams 33

4.3 - Relationship between objects 40

CHAPTER 5 41Object Orientated Analysis (OOD) 41

5.1Inner details of class attributes and methods 41

CHAPTER 6 48

CHAPTER 7 49


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CHAPTER 8 51

8.1 - System requirements 51

8.2 - The Face Recognition System project directory 52

8.3 - Running the Access Control system 548.4Complete user interface 54

8.4.1 - How the user interface behaves 57

8.4.1 - The Add User Dialog 58

8.4.2 - The Confirm User Delete dialog 61

8.4.3 - The File menu 62

8.4.4 - The Face Detection menu 63

8.4.5Face Detection Settings dialog 64

8.4.6The Help menu 65

8.4.7The User Guide dialog 66

8.4.8The About dialog 67

Chapter 9 69

CONCLUSION 69


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DECLARATION

I declare that A FACE DETECTION SYSTEM USED FOR ACCESS CONTROL is

my own work, that it has not been submitted for any degree or examination in any other

university and that all sources I have used or quoted have been indicated and

acknowledged by complete references.

Dmitri Warren De Klerk November 2009

Signature: .


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KEYWORDS

Haar Features

Integral Image

Cascade

AdaBoost

Boosting

Classifier

Weak Classifier

Strong Classifier


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ACKNOWLEDGMENTS

I thank the Almighty God, for its through his strength; I can do all things through Christ

who strengthens me. Thanks to my supervisor Mr. J. Connan for his patience, support

and guidance and his input in this project. Thanks to my family and girlfriend who has

supported and motivated me.

Thanks to the staff members of the Department of Computer Science and my classmates

for their kind assistance during the year.

I also would like to acknowledge the work done by Desmond Eustin van Wyk.. His

documentation proved extremely useful in documenting my work.


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GLOSSARY

AdaBoost (Adaptive Boosting): AdaBoost is an efficient machine learning boosting

algorithm which combines weak classifiers while reducing significantly not only the

training error but also the more elusive generalization error.

Boosting: A method of producing a very accurate prediction rule by combining rough

and moderately inaccurate rules of thumb.

Base Resolution:The resolution at which the detector starts to detect faces.

Weak Classifier:The classifier is called weak because, they only need to classify correctly

the examples in more of 50% of the cases.


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INTRODUCTION

In this project a face detection system is implemented and integrated into an Access

Control system. Face detection systems locate the size and scale of human faces in images

and video sequence, if present. Face detection is the first step for Face localization, Face

Tracking, Facial Expression recognition, and Face Recognition.

Face detection in itself is a challenging problem. The difficulty resides in the face that faces

are non rigid objects. Face appearance may vary between two photographs of the same

person, depending on the emotional stage, lighting conditions or pose[4]. This is why so

many methods have been developed in the past years.

The goal is to detect very quickly faces in cluttered backgrounds. This situation can be

found in many applications as surveillance of public places, common Access Control

conditions. Thus far learning-based approaches have been the most effective and have

therefore attracted a lot of attention the last years. Viola and Jones [6][7] introduced an

impressive face detection system capable of detection frontal-view faces in real time. This

is attributed to the AdaBoost learning algorithm presented by Freund and Schapire [8] and

the response of simple

features used by Viola and Jones [6]. Hundreds of features can quickly be calculated by

introducing a new image representation called the Integral Image.The Adaboost

algorithm sequentially contructs a classifier as a linear combination of weak classifier.

The classifiers are combined in a cascade which allows background regions to be quickly

discarded while spending more computation on more promising object like reqions.


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C H A P T E R 1

USERS REQUIREMENTS DOCUMENT (URD)

This document describes the problem from the user's point of view. It briefly describes

the problem domain of face detection. Then the document delivers a simple and exact

problem description, wherein the user states exactly what he/she would like the face

detection system to do. We focus on the tasks to be solved rather than the interface

required solving them.

1.1 - Users view of the problemThe user requires a face detection system given an image, the goal of the face detection

system is to determine whether or not there are any faces in the image and, if present,

return the face location. The system should operate at real-time to make for a passive and

fully automatic Access Control system. A user would only be required to stand in front of

the camera in order to be recognized. The main motivation for a face detection system is

that the user wouldnt be required to position his/her face into a fixed size box in order to

be recognized by the face recognition system.

Other reasons for a face detection system are that it is the first step:

Face localization seeks to determine the position of a single face within an image; the

detection problem is simplified since the input image contains only one face.

Facial feature detectionseeks to detect the presence and location of features, such as the

mouth, nose, eyes, lips, ears, etc.; the detection problem is simplified since the input image

contains only one face.

Facial expression recognitionidentifies the emotional states of humans, e.g. happy, sad,anger.

Face trackingmethods estimates the location and possibly the orientation of a face in an

image on a continuous basis within real time.


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As proven above face detection is the first step in any fully automated system which solves

the above problems, therefore a robust and accurate face detector system is critical.

1.2 - Problem domain

Given an image the goal of a face detection system is to identify all face regions regardless

of its position and scale [1]. This problem is challenging as faces are non rigid objects. Face

appearance may vary between two different persons and also between two images of the

same person, depending on the lighting conditions, emotional state and pose of the

subject [4].

Face detection difficulties:

The face global attributes. Some common face attributes from every face. A face can

globally be estimated by a kind of ellipse, but humans have thin faces, round faces, etc.

Skin color is also different from one person to another.

The facial expression. Face appearance is highly depends on emotional state of people.

Face features of a smiling face is far from those of an indifferent temperament or a sad

face.

Presence or absence of structural components. Face detection included objects thatcan be found on a face: glasses which change one of the main characteristics of the faces,

the darkness of the eyes. Natural facial features: beards, mustaches or can occult part of

the face.

1.3 - Complete description of the problem

Determine whether or not there are any faces in the camera output and, if present, return

the face locations of the images. The biggest face detected, being the user closest to the

camera are scaled to a recognizable scale. This detection window is then passed to the facerecognition system implemented by Desmond Eustin van Wyk [3] for recognition.


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1.4 - Expectations from a system

The first and foremost expectation for a face recognition system is that it must have a high

degree of accuracy when recognizing people. The next highest expectation from a system

is that people should be indicated who they are when the system recognized them. It must

also be possible to add and remove people from the system that should be recognized.

The system is to accurately identify and locate human faces under the following conditions

and circumstances:

upright, frontal faces

minor variations in lighting conditions and

minor variations in facial expression.

minor variation in illumination

big enough scale in order to perform face recognition.

any position

1.5 - Not expected from a system

A solution system is not expected to detect a human face under the following conditions

and circumstances:

non frontal face pose rotated faces extreme lighting conditions

darkness too much light


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C H A P T E R 2

REQUIREMENTS ANALYSIS DOCUMENT (RAD)

This document takes the user requirements as a starting point and looks at face detection

from the designers view. The analysis focuses on the system and software needed to

implement the user requirements. We take the users requirements and clearly identify all

of the details and mitigating factors that will affect the solution the user wants. The RAD

then identifies the software system and paradigm that will best fit the user requirements.

2.1 - Users requirements interpretationAccess control systems are considered to be mission-critical and real-time systems and

thus must operate correctly under many different situations and circumstances. For a fully

automatic face recognition system, face detection and face localization are very important

and the very first steps to developing such a system[3]. The background composition is

one of the main factors for explaining the difficulties in face detection [4]. Face detection

in access control systems need to detect faces in any background, meaning the background

can be textured and with great variability [4]. The single most dominant problem with a

face recognition system or other biometric systems is accuracy and they do not perform

well under the many different situations and circumstances that are encountered in day-to-

day life [3].

The two most important characteristics for a face detector are its detection and error rate.

The detection rate of a face detector is defined as the ratio between the number of

correctly detected and the number of actual faces. The error can be broken down into two

types of error namely;

False positivesan image sub-region is declared to be a face, but is not.

False negativesan image sub-region is not declared as a face, when it is a face.


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2.2 - Existing solutions

Face detection is the first step to any fully face recognition, face localization, face

expression recognition system, etc. Thus face detection has been highly researched in the

past years, there are many different techniques and algorithms for performing face

detection. All these techniques fall under the following main methods.

Knowledge-based methods

Based on what constitutes a typical face. e.g. the relationship between facial

features.

Feature invariant approaches

Finds structural features of a face that exist even when the viewpoint, lighting or

pose vary.

Template matching methods:

Uses several standard patterns to describe the face as a whole or the facial features

separately.

Appearance based methods (classifiers/learning-based):

The models are learned from a set of training images that capture the

representative variability of facial appearance.

2.3 - Suggested system

Detecting faces in black and white, still images with unconstrained, complex backgrounds

is a complicated task [1]. Thus far, learning-based approaches have been most effective

and have therefore attracted a lot of attention over the last years. In 2001 Viola and Jones

[6][7] published an impressive face detector system capable of detecting frontal-view facesin real time. The properties of the detector are partly attributed to the AdaBoost learning

algorithm.


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AdaBoost (Adaptive Boosting) was rapidly made popular in the machine learning

community when it was presented by Freund and Schapire [8].

As we want to detect faces in various backgrounds found in Access Control systems, it

would be improper to use purely geometrical methods. In fact the main advantage to these

geometrical methods is the geometric invariant properties. We are not interested by them

since were staying in the context of frontal face detection. So it is quite naturally that we

have oriented our choice towards learning algorithms. Boosting is a powerful iterative

procedure that builds efficient classifiers by selecting and combining very simple classifiers.

The suggested system uses Boosting and Haar features (features).

The first step is to compute an image representation called an integral image that allows

for very fast feature evaluation. In order to compute these features very rapidly at many

scales we introduce the integral image representation for images. The integral image can be

computed from an image using a few operations per pixel. Once computed; any one of

these Haar-like features can be computed at any scale or location in constant time[7].

The second contribution of this paper is a method for constructing a classifier by selecting

a small number of important features using AdaBoost [6]. AdaBoost is used to both select

and train the classifier. AdaBoost then boost the performance of weak classifier.

The third major contribution of this paper is a method for combining successively more

complex classifiers in a cascade structure which dramatically increases the speed of the

detector by focusing attention on promising regions of the image [7].

2.4 - Testing the suggested system

The system can be tested on the MIT+CMU frontal face test set [6]. This set consists of

130 images with 507 labeled frontal faces.

The image dataset is used by the CMU Face Detection Project and is provided for

evaluating algorithms for detecting frontal views of human faces.
http://www.ri.cmu.edu/projects/project_320.htmlhttp://www.ri.cmu.edu/projects/project_320.html


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C H A P T E R 3

USERS INTERFACE SPECIFICATION

This chapter describes exactly what the user interface is going to do, what it looks like, and

how the user interacts with the program. The UIS however does not describe how the

interface is implemented. Nor does it describe what the program does behind the

interface. Rather, the UIS focuses in detail specifically on the user interface itself.

3.1Complete user interface

Figure 3.1 below displays the main graphical user interface frame for our system titled

Access Control.

Figure 3.1: Complete user interface


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User Image panel

The User Image panel displays the user image of the user currently selected in the Userlist

panel.

User List panel

The User List panel lists the authorized users the Access Control system can recognize.

Search field

The search field can be used to search for a user in the face recognition system. This

feature can be very useful when the face recognition system have hundreds of users.

The search function searches for the substring of the search text in the username and

lists all the matched usernames in alphabetical order, displaying the first matched users

image in the user image panel.

Camera output

The camera output displays the cameras output. Used for capturing images when adding

users to the face recognition system, and for monitor current user login activity.

Add user button

The add user button adds an authorized user to the Access Control system. On execution

of the Add User button the Add User dialog as shown in Figure 3.5 is displayed.

Remove user buttonThe remove button is only enabled when there are users in the system. The remove button

removes a user from the Access Control system. On execution of the Remove User

button the Confirm User Delete dialog in Figure 3.8 is displayed.

Recognition output

The Access Control system displays the output of the recognized user below camera

output. When a user is not recognized by the Access Control system, the system displays

Who are you? , otherwise the system displays the user ID of the recognized user and the

rate of the face recognition.


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Log panel

When a user is detected by the face detection system and recognized by the face

recognition system, the Access Control system logs the users information to the log panel.

The log panel logs the user and the time at which the user logged in, the log file gets saves

with the current date every time the user terminates the system.

Acceptance threshold

The face recognition system has a threshold at which users should be recognized. With 0

being the lowest threshold value and with 1 being the highest threshold value. With 0

being the most lenient and 1 being the most strict at which the recognition operates. The

Acceptance threshold spinner is used to adjust the acceptance threshold value used with

which the ANNs output is compared[1].


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3.1.1 - How the user interface behaves

When the face detection system does not detect any faces or detects a false positive or the

face recognition system does not recognize the user. Then the system displays Who are

you?As displayed in Figure 3.3 below.

Figure 3.3: How the user interface behaveswith detectionand no recognition.

When the users face is detected and recognized, the system will display the user ID and

the recognition rate as displayed in Figure 3.4 below.

Figure 3.4: How the user interface behaveswith detectionand recognition


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3.1.2 - The Add User Dialog

The Add User Dialog is displayed in Figure 3.5 below, this frame is displayed when the

Add User button in the Access Control GUI is pressed or when the Add User menu item

from the File menu is selected. The location of the Add User Dialog is such that the

cameras output can be clearly viewed. Each interface component in the Add User Dialog

with its purpose or action is described in Table 3.1.

Figure 3.5: The Add User dialog.Add User dialog

Interface component Purpose/Action

User Image panel Displays the captured image.

Capture Image button Captures an image.

ID label and text field The text field where a user identifier should

be entered.

Ok button Add the user with the specified identifier

that should be recognized. If the ID text

field is empty the error dialog in Figure 3.6

is displayed. If no images was captured the

error dialog in Figure 3.7 is displayed.

Cancel button Do not add the user, discard the captured

image and close the Add User dialog.

Table 3.1: The Add User dialog interface componentsdescribed.


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From the add user dialog, if a user clicks Ok without entering a user ID or capturing an

image. The following dialog in Figure 3.6 below is displayed.

Figure 3.6: Error dialog displayed for empty ID text field or nocaptured image.

From the add user dialog, if a user clicks Ok without capturing an image. The following

dialog in Figure 3.7 below is displayed.

Figure 3.7: Error dialog displayed when no image wascaptured.


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3.1.3 - The Confirm User Delete dialog

The Confirm User dialog is displayed in Figure 3.8 below, this dialog is displayed when

selecting a user in the User List panel and clicking the Remove User button. When

removing a user from the system, the default option is set to no, such that the

administrator doesnt blindly remove a user from the Access Control system.

Figure 3.8: Confirm User Delete dialog displayed when clickingthe remove user button.

Confirm User Delete dialog


Yes button Completely removes the user from the

Access Control system.

No button Do not remove the user, close the

confirmation dialog.

Table 3.2: The Confirm User Delete dialog interfacecomponents described.


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3.1.4 - The File menu

The complete File menu with the menu items it contains is displayed in Figure 3.9 below.

Each menu item of the File menu with its associated action is described in Table 3.3.

Figure 3.9: The File menu.

The File menu

Menu item Action

Add User Adds a user to the Access Control system.Exit Exits the Access Control system.

Table 3.3: The File menu items described.


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3.1.5 - The Face Detection menu

The complete Face Detection menu with the menu items it contains is displayed in Figure

3.10 below. Each menu item of the Face Detection menu with its associated action is

described in Table 3.4.

Figure 3.10: The File Detection menu.

The Face Detection menu

Menu item Action

Settings Opens the Face Detection Settings dialog as

displayed in Figure 3.11.Table 3.4: The File menu items described.


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3.1.7The Help menu

The complete Help menu with the menu items it contains is displayed in Figure 3.12. Each

menu item of the Help menu with its associated action is described in Table 3.5.

Figure 3.12: The Help menu.

The Help menu

Menu item Action

User Guide Open the Users Guide dialog displayed

in Figure 3.13.

About Open the About dialog displayed in

Figure 3.14.

Table 3.5: The Help menu items described.


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3.1.8The User Guide dialog

The Users Guide dialog is a modal dialog that is displayed in Figure 3.13. This dialog

contains the Users Guide for the Administrator. It is opened when the Users Guide menu

item from the Help menu is selected. The Users Guide dialog can be closed by either the

close button at the top right corner or the Ok button at the bottom of the dialog[1].

Figure 3.13: The Users Guide dialog.


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3.1.9The About dialog

The About dialog is a modal dialog that is displayed in Figure 3.13. This dialog contains a

message about the Access Control system. The About dialog can be closed by either the

close button at the top right corner or the OK button at the bottom of the dialog.

Figure 3.14: The About dialog.


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Figure 3.15 below displays the webcam that Users interact with and that is also used to

capture face images.

Figure 3.15: The webcam that Users interact with and that isalso used to capture face images.


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C H A P T E R 4

OBJECT ORIENTATED ANALYSIS (OOA)

In this chapter we apply an object-oriented view to the face detection system. We begin by

providing a detailed description of the objects in the form of a data directory. In addition

we provide detailed class diagrams, identifying class attributes and methods. Finally, we

present the relationships between objects.

4.1 - Data dictionary

The data dictionary describes the Face detection system objects in detail, each object isdocumented. We provide a clean understanding of each object in terms of the functions

they perform in the form of a detailed description.

Class Description

adaboostTrain This class trains a weak classifier using the AdaBoostmachine learning algorithm presented by Freund &Schapire [8]. Once trained the weak classifier is saved tothe database of weak classifiers.

buildCascadeClassifier This class builds a cascade classifier which achievesincreased performance while radically improvingcomputation time. The key insight is, smaller moreefficient classifiers are constructed which reject many ofthe negative sub windows while detecting all positiveimage instances. Simple classifiers are used to reject themajority of sub-windows before more complexclassifiers are called upon to decrease the false positive[6][7]. The stages in the cascade classifier areconstructed by training classifiers using AdaBoost andthen adjusting the threshold to minimize the falsenegatives [6][7].

cascadeClassifier This class represents a complete cascade classifier withall its stages. The cascade classifier is a chain/array ofcascade stages. Recall that each stage in the cascade


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classifier is a smaller more efficient boosted classifier.The form of the cascade classifier is that of adegenerate decision tree. A positive result from the firstclassifier triggers the evaluation of a second classifier,and so on. A negative result at any point leads to

immediate rejection of the sub-window[6][7].cascadeStage This class represents a cascade classifier stage used in

the cascade classifier class. The cascade classifier stageis an AdaBoost strong classifier with a fairly smallnumber of weak classifiers. This is such that thedetector can quickly distinguish if an image sub regionis a "Face" or a "Non-face". Each stage in the cascadeclassifier is trained by adding weak classifiers until thetarget detection and false positive rates are met. Thestage threshold is adjusted to accept all face example inthe training set, while minimizing the false negatives.

feature The class represents simple features, our detectorclassifies images based on the value of simple features;These features, which have also been used by Viola andJones, are also known as Haar features, or Haar-likefeatures. We use 5 kinds of features. Given the baseresolution of the detector is 19x19 our detector has anexhaustive set of 67209 features.

integralImage This class determines the integral image of a giveninput PGMImage. The integral image can beconsidered as a means to quickly compute the rectanglefeatures. This intermediate representation for the inputPGMImage at location (x, y) is the sum of all the pixelsabove and to the left of (x, y). Making use of theintegral image any rectangular sum can be computed infour array references.

integrateMultipleDetections This class represents a final detection; since the finaldetection is insensitive to small changes in translationand scale, multiple detections will normally occuraround each face in a scanned image. This class returnsin one final face detection per face by combiningclusters of overlapping detections into a single

detection. In addition this class returns the biggest facedetection used by the face recognition system.

scanDetector The final detector is scanned across the 320x240 imagesequences at multiple scales and locations. The detectoritself is scaled, rather than the image. The detector is


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scanned across locations by shifting the detector by twopixels horizontally and vertically. The choice of thisshifting affects both speed and accuracy of the detector.The two pixels shifting promised good results inexperiments. In addition this class passes the biggest

detection to the face recognition system.trainImage This class represents a training image for the AdaBoost

machine learning algorithm. Each training image has anintegral image that represents the object of interest. Thetype being a positive or negative image. The weight ofthe training image is used for training the face detector.The weight is used by AdaBoost; the weight is ameasure of how important the training image is.

weakClassifier This class represents a weak classifier; the classifier iscalled weak because the classifier is only expected toclassify 50% of the training set images correctly. The

features are used to build the weak classifiers, A weakclassifier is a feature, with the extend of a classifier error how bad the classifier is, tested on a validation set,and a classifier weight how good the classifier is,tested on the positive training set.

Table 4-1: Data Dictionary - the objects combined with a briefdescription of each.


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4.2 - Class diagrams

The class diagrams contain the name of the class; its attributes as well as the associated

methods of the class. The type of the class attributes, the return types of the methods as

well as the class method parameters.

Figure 4.1: adaboostTrain Classtrains a weak classifier usingthe AdaBoost algorithm.


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Figure 4.2: buildCascadeClassifier Classtrains a cascadeclassifier used by the face detector for face detection.


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Figure 4.3: cascadeClassifier ClassThe trained cascadeclassifier used by the face detector for face detection.

Figure 4.4: cascadeStage ClassA smaller more efficientboosted classifier used by the cascade classifier.


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Figure 4.5: feature ClassOur detector classifies images basedon these features.

Figure 4.6: integralImage Classdetermines the integral image,in order to quickly compute feature response values.


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Figure 4.8: scanDetector Classscans the detector at multiplescales and locations and passes the biggest detection to the face

recognition system.


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4.3 - Relationship between objects

The following Figure 4.11 represents the relationship between the objects indicates how

the objects interact with each other or how they are related to each other.

Figure 4.11: Relationship between classes


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C H A P T E R 5

OBJECT ORIENTATED ANALYSIS (OOD)

The document is as close to coding as you can get without actually coding. This document

takes the classes in the Object Oriented Analysis deeper into the realm of pseudo-code.

5.1Inner details of class methods

The inner details of class method in detail, each method is documented to give a more

detailed description of the class object.

Class Method Description

adaboostTrain determineNumPosAndNeg()This method determines the number of negative and positive

images the training set consists of.

getTrainingSet()This method gets the Adaboost training set.

getWeakClassifierCounter()This method returns the number of weak classifiers trained thus

far in the weak classifier database.

initializeWeights()This function initializes the weight uniformly over the training

data, the sum of the weights of all images in the trainingSet equals 1.

normalizeWeights(java.util.Vector training

Set)This method normalize the weights of the training set, such that

the weights is a probability distribution: Sum(all weights) = 1

printWeights(java.util.Vector trainingSet)This method is used for debugging.

setupTrainingSet()This function sets the positive and negative images of thetraining set.

setWeakClassifierCounter(int weakClassifierCounter)This method sets the Adaboost training set.
http://localhost/var/www/apps/conversion/tmp/faceDetection/adaboostTrain.html#determineNumPosAndNeg()http://localhost/var/www/apps/conversion/tmp/faceDetection/adaboostTrain.html#determineNumPosAndNeg()http://localhost/var/www/apps/conversion/tmp/faceDetection/adaboostTrain.html#getTrainingSet()http://localhost/var/www/apps/conversion/tmp/faceDetection/adaboostTrain.html#getWeakClassifierCounter()http://localhost/var/www/apps/conversion/tmp/faceDetection/adaboostTrain.html#initializeWeights()http://localhost/var/www/apps/conversion/tmp/faceDetection/adaboostTrain.html#normalizeWeights(java.util.Vector)http://localhost/var/www/apps/conversion/tmp/faceDetection/trainImage.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/adaboostTrain.html#printWeights(java.util.Vector)http://localhost/var/www/apps/conversion/tmp/faceDetection/trainImage.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/trainImage.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/adaboostTrain.html#setupTrainingSet()http://localhost/var/www/apps/conversion/tmp/faceDetection/adaboostTrain.html#setWeakClassifierCounter(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/adaboostTrain.html#setWeakClassifierCounter(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/adaboostTrain.html#setupTrainingSet()http://localhost/var/www/apps/conversion/tmp/faceDetection/trainImage.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/adaboostTrain.html#printWeights(java.util.Vector)http://localhost/var/www/apps/conversion/tmp/faceDetection/trainImage.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/adaboostTrain.html#normalizeWeights(java.util.Vector)http://localhost/var/www/apps/conversion/tmp/faceDetection/adaboostTrain.html#initializeWeights()http://localhost/var/www/apps/conversion/tmp/faceDetection/adaboostTrain.html#getWeakClassifierCounter()http://localhost/var/www/apps/conversion/tmp/faceDetection/adaboostTrain.html#getTrainingSet()http://localhost/var/www/apps/conversion/tmp/faceDetection/adaboostTrain.html#determineNumPosAndNeg()


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trainClassifierWithAdaBoost()This method trains a weak classifier using AdaBoost and writes

the weak classifier to the database of weak classifiers.

updateNegativeTrainingImages(java.util.Vector newNegatives)

This method updates the negative training images, by addingthe input vector of negatives to the training set.

updateWeight(weakClassifierweakClassifiers,

java.util.Vector trainingSet)This method updates the weight of the training set.

baseLearner allFeatures(int scale)This method calculates all possible features that can fit into a

given image of width and height specified.

baseLearner(java.util.Vector trainingSet)This method returns the weak classfier with the lowest training

error on the training set.

calculateOptimalThresholdValues (java.util.Vector trainingSet)

This method determines the optimal thresold values for all thefeatures.

evaluateError(int featureOptThreshold,

double lowestError)This method evaluates the weighted error of a feature over the

training set, such that we can choose the weak classifier with thelowest error.

getAllFeatures()This method gets the features used to choose a weak classifier

from.getFeatureOptimalThreshold()

This method sets the feature optimal threshold values, for allthe features.

initializeValues()This method initialize the features response values as well as the

feature optimal threshold values.

optimalThreshold()This method is used to determine the optimal threshold over

the training set.

removeFeatures(int featureNum)

This method removes a feature from being used.setAllFeatures(java.util.Vector allFeatures)

This method sets the features used to choose a weak classifierfrom.

setFeatureOptimalThreshold(java.util.Vector


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Integer> featureOptimalThreshold)This method sets the feature optimal threshold values, for all

the features.

totalHScales(int imageWidth, featureevaluateFeature)This method determines the total number of scales at which the

input feature can be applied within a given imageWidth.buildCascadeClas

sifier

buildCascadeClassifier()This method build a cascade classifier using the AdaBoost

machine learning algorithm.

calculateTotalWeightForStage()This method calculates the total weight for the current stage,

from where the stage start to where the stage end.

evaluateD()This method calculates the values D the detection rate of the

cascade classifier on a the positive training images.

evaluateF(boolean getThresholdValues)

This method calculates the value F - the false positive rate ofthe cascade classifier on a validation set.

initializeThresholdValues()This method initialize the thresholdValues array.

smartTrainWrite(java.util.Vector trainingS

et, java.lang.String filename)

stageThreshold()The initial AdaBoost threshold is designed to yield a low error

rate on the training data.

cascadeClassifier addStage(cascadeStagestage)This method adds a stage to the cascade classifier.

getStage(int stageNum)This method returns the cascade classifier stage at the given

stageNum.

readCascade(java.lang.String filename)This method reads the cascade classifier from the file system.

stageEdit(int stageNum, cascadeStagenewStage)

toFile()This method is used by the writeCascade method.

toString()This method prints the cascade classifier to a string.

totalStages()This method returns the total number of stages in the cascade

classifier.
http://localhost/var/www/apps/conversion/tmp/faceDetection/baseLearner.html#totalHScales(int,%20faceDetection.feature)http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/buildCascadeClassifier.html#buildCascadeClassifier()http://localhost/var/www/apps/conversion/tmp/faceDetection/buildCascadeClassifier.html#calculateTotalWeightForStage()http://localhost/var/www/apps/conversion/tmp/faceDetection/buildCascadeClassifier.html#evaluateD()http://localhost/var/www/apps/conversion/tmp/faceDetection/buildCascadeClassifier.html#evaluateD()http://localhost/var/www/apps/conversion/tmp/faceDetection/buildCascadeClassifier.html#evaluateF(boolean)http://localhost/var/www/apps/conversion/tmp/faceDetection/buildCascadeClassifier.html#evaluateF(boolean)http://localhost/var/www/apps/conversion/tmp/faceDetection/buildCascadeClassifier.html#initializeThresholdValues()http://localhost/var/www/apps/conversion/tmp/faceDetection/buildCascadeClassifier.html#smartTrainWrite(java.util.Vector,%20java.lang.String)http://localhost/var/www/apps/conversion/tmp/faceDetection/trainImage.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/buildCascadeClassifier.html#stageThreshold()http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeClassifier.html#addStage(faceDetection.cascadeStage)http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeStage.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeStage.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeStage.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeClassifier.html#getStage(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeClassifier.html#readCascade(java.lang.String)http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeClassifier.html#readCascade(java.lang.String)http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeClassifier.html#stageEdit(int,%20faceDetection.cascadeStage)http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeClassifier.html#stageEdit(int,%20faceDetection.cascadeStage)http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeStage.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeClassifier.html#toFile()http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeClassifier.html#toString()http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeClassifier.html#totalStages()http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeClassifier.html#totalStages()http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeClassifier.html#totalStages()http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeClassifier.html#toString()http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeClassifier.html#toFile()http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeStage.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeClassifier.html#stageEdit(int,%20faceDetection.cascadeStage)http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeClassifier.html#readCascade(java.lang.String)http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeClassifier.html#getStage(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeStage.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeClassifier.html#addStage(faceDetection.cascadeStage)http://localhost/var/www/apps/conversion/tmp/faceDetection/buildCascadeClassifier.html#stageThreshold()http://localhost/var/www/apps/conversion/tmp/faceDetection/trainImage.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/buildCascadeClassifier.html#smartTrainWrite(java.util.Vector,%20java.lang.String)http://localhost/var/www/apps/conversion/tmp/faceDetection/buildCascadeClassifier.html#initializeThresholdValues()http://localhost/var/www/apps/conversion/tmp/faceDetection/buildCascadeClassifier.html#evaluateF(boolean)http://localhost/var/www/apps/conversion/tmp/faceDetection/buildCascadeClassifier.html#evaluateD()http://localhost/var/www/apps/conversion/tmp/faceDetection/buildCascadeClassifier.html#calculateTotalWeightForStage()http://localhost/var/www/apps/conversion/tmp/faceDetection/buildCascadeClassifier.html#buildCascadeClassifier()http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/baseLearner.html#totalHScales(int,%20faceDetection.feature)


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writeCascade(cascadeClassifiercascade,

java.lang.String filename)This method saves the cascade classifier to the file system.

cascadeStage getThreshold()This method gets the threshold of the stage.

getTotalClassifiers()This method gets the total number of weak classifiers there arein this stage.

getweakClassifierStart()This method gets the start of the weak classifiers in the weak

classifier database for this stage.

increaseTotalClassifiers()This method increase the total number of weak classifiers there

are in this stage.

setThreshold(double threshold)This method sets the threshold of the stage.

setTotalClassifiers(int weakClassifierTotal)This method sets the total number of weak classifiers there are

in this stage.

setweakClassifierStart(int weakClassifierStart)This method sets the start of the weak classifiers in the weak

classifier database for this stage.

feature calculateFeature(int[][] integralimage,int initialScale, int currentScale, int x, int y)

This method calculates the feature response value of thisfeature on the input integral image.

getHeight()

This method gets the height of the feature.getHeightScale()

This method gets the heightScale of the feature.

getOptimalThreshold()This method gets the optimal threshold of the feature.

getWidth()This method gets the width of the feature.

getWidthScale()This method gets the widthScale of the feature.

getX()

This method gets the x(top/left) column location of thefeature.

getY()This method gets the y(top/left) row location of the feature.

I(int[][] integralImage, int xCoordinate,
http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeClassifier.html#writeCascade(faceDetection.cascadeClassifier,%20java.lang.String)http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeClassifier.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeClassifier.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeStage.html#getThreshold()http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeStage.html#getTotalClassifiers()http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeStage.html#getweakClassifierStart()http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeStage.html#increaseTotalClassifiers()http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeStage.html#setThreshold(double)http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeStage.html#setTotalClassifiers(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeStage.html#setweakClassifierStart(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#calculateFeature(int[][],%20int,%20int,%20int,%20int)http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#getHeight()http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#getHeight()http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#getHeightScale()http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#getOptimalThreshold()http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#getWidth()http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#getWidthScale()http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#getX()http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#getY()http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#I(int[][],%20int,%20int)http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#I(int[][],%20int,%20int)http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#I(int[][],%20int,%20int)http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#getY()http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#getX()http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#getWidthScale()http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#getWidth()http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#getOptimalThreshold()http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#getHeightScale()http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#getHeight()http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#calculateFeature(int[][],%20int,%20int,%20int,%20int)http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeStage.html#setweakClassifierStart(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeStage.html#setTotalClassifiers(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeStage.html#setThreshold(double)http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeStage.html#increaseTotalClassifiers()http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeStage.html#getweakClassifierStart()http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeStage.html#getTotalClassifiers()http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeStage.html#getThreshold()http://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeClassifier.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/cascadeClassifier.html#writeCascade(faceDetection.cascadeClassifier,%20java.lang.String)


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int yCoordinate)This method returns the integral image values in case, x = -1 or

y = -1 for the following condition: I(-1, y) = I(x, -1) = I(-1,-1) = 0,else it just returns the integral image value at x and y respectively.

setHeight(int height)

This method sets the height of the feature.setHeightScale(int heightScale)

This method sets the heightScale of the feature.

setOptimalThreshold(int optimalThreshold)This method sets the optimal threshold of the feature.

setWidth(int width)This method sets the width of the feature.

setWidthScale(int widthScale)This method sets the widthScale of the feature.

setX(int xCoordinate)This method gets the x(top/left) column location of the

feature.

setY(int yCoordinate)This method gets the y(top/left) row location of the feature.

toString()This method prints the feature to a string.

integralImage I(int x, int y)This function returns the integral image values in case, x = -1 or

y = -1 for the following condition: I(-1, y) = I(x, -1) = I(-1,-1) = 0,else it just returns the integral image value at x and y respectively.

integralImage(PGMImagepgm)

This function takes as input a pgm image and determines thecorresponding integral image of the image.

printIntegralImage(int[][] integralImage)This method is used for debugging.

integrateMultiple

Detections

biggestDetection()This method returns the biggest detection of all the detections.

clusterizeDetections()This method puts the detections into clusters - a cluster is

defined as detections which overlaps.

integratedDetections(boolean print)Each partition yields a single final detection.The corners of the

final bounding region are the average of the corners of all detectionsin the set.

integrateMultipleDetections(java.util.Vector multipleDetections, byte[] outData, int cy,

int cY, int cx, int cX, int lineStride,
http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#setHeight(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#setHeight(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#setHeightScale(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#setOptimalThreshold(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#setWidth(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#setWidthScale(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#setX(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#setY(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#toString()http://localhost/var/www/apps/conversion/tmp/faceDetection/integralImage.html#I(int,%20int)http://localhost/var/www/apps/conversion/tmp/faceDetection/integralImage.html#I(int,%20int)http://localhost/var/www/apps/conversion/tmp/faceDetection/integralImage.html#integralImage(icp.PGMImage)http://localhost/var/www/apps/conversion/tmp/icp/PGMImage.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/integralImage.html#printIntegralImage(int[][])http://localhost/var/www/apps/conversion/tmp/faceDetection/integrateMultipleDetections.html#biggestDetection()http://localhost/var/www/apps/conversion/tmp/faceDetection/integrateMultipleDetections.html#clusterizeDetections()http://localhost/var/www/apps/conversion/tmp/faceDetection/integrateMultipleDetections.html#integratedDetections(boolean)http://localhost/var/www/apps/conversion/tmp/faceDetection/integrateMultipleDetections.html#integrateMultipleDetections(java.util.Vector,%20byte[],%20int,%20int,%20int,%20int,%20int,%20int)http://localhost/var/www/apps/conversion/tmp/faceDetection/detectionWindow.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/detectionWindow.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/detectionWindow.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/detectionWindow.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/detectionWindow.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/integrateMultipleDetections.html#integrateMultipleDetections(java.util.Vector,%20byte[],%20int,%20int,%20int,%20int,%20int,%20int)http://localhost/var/www/apps/conversion/tmp/faceDetection/integrateMultipleDetections.html#integratedDetections(boolean)http://localhost/var/www/apps/conversion/tmp/faceDetection/integrateMultipleDetections.html#clusterizeDetections()http://localhost/var/www/apps/conversion/tmp/faceDetection/integrateMultipleDetections.html#biggestDetection()http://localhost/var/www/apps/conversion/tmp/faceDetection/integralImage.html#printIntegralImage(int[][])http://localhost/var/www/apps/conversion/tmp/icp/PGMImage.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/integralImage.html#integralImage(icp.PGMImage)http://localhost/var/www/apps/conversion/tmp/faceDetection/integralImage.html#I(int,%20int)http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#toString()http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#setY(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#setX(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#setWidthScale(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#setWidth(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#setOptimalThreshold(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#setHeightScale(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.html#setHeight(int)


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int pixelStride)This method takes as input a Vector of detections of faces.

printDetection(detectionWindowdetectWindow)Takes as input a detection and prints a white border around the

detected face.

scanDetector loadCascade()This method loads in the cascade classifier from the file system.

scanDetector(PGMImageinputImage, byte[] outData,

int cy, int cY, int cx, int cX, int lineStride,

int pixelStride)This method runs the detector(cascade classifier) over the

camera output images.

trainImage getIntegralImage()This method gets the integral image for this trainImage.

getType()This method gets the type of an image, if its a positive or a

negative image.getWeight()

This method gets the weight of an image in the trainingdatabase over the training set.

setIntegralImage(int[][] image)This method sets the integral image for this trainImage.

setType(int type)This method sets the type of an image, if its a positive or

negative image.

setWeight(double weight)This method sets the weight of an image in the training

database over the training set.weakClassifier determineAndSetClassifierWeight (double error)

This method determines and sets the weight of the classifier,since hypothesis weight = 1/2 ln (1-error)/error

getClassifierError()This method gets the error of this classifier.

getClassifierFeature()This method gets the feature of a weak classifier.

getClassifierWeight()This method gets the weight of the classifier.

readWeakClassifier(java.lang.String filename)This function reads the weakClassifier from a file.

setClassifierError(double classifierError)This method sets the error for this classifier.

setClassifierFeature(featureclassifierFeature)
http://localhost/var/www/apps/conversion/tmp/faceDetection/integrateMultipleDetections.html#printDetection(faceDetection.detectionWindow)http://localhost/var/www/apps/conversion/tmp/faceDetection/detectionWindow.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/detectionWindow.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/scanDetector.html#loadCascade()http://localhost/var/www/apps/conversion/tmp/faceDetection/scanDetector.html#loadCascade()http://localhost/var/www/apps/conversion/tmp/faceDetection/scanDetector.html#scanDetector(icp.PGMImage,%20byte[],%20int,%20int,%20int,%20int,%20int,%20int)http://localhost/var/www/apps/conversion/tmp/icp/PGMImage.htmlhttp://localhost/var/www/apps/conversion/tmp/icp/PGMImage.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/trainImage.html#getIntegralImage()http://localhost/var/www/apps/conversion/tmp/faceDetection/trainImage.html#getType()http://localhost/var/www/apps/conversion/tmp/faceDetection/trainImage.html#getWeight()http://localhost/var/www/apps/conversion/tmp/faceDetection/trainImage.html#getWeight()http://localhost/var/www/apps/conversion/tmp/faceDetection/trainImage.html#setIntegralImage(int[][])http://localhost/var/www/apps/conversion/tmp/faceDetection/trainImage.html#setType(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/trainImage.html#setWeight(double)http://localhost/var/www/apps/conversion/tmp/faceDetection/trainImage.html#setWeight(double)http://localhost/var/www/apps/conversion/tmp/faceDetection/weakClassifier.html#determineAndSetClassifierWeight(double)http://localhost/var/www/apps/conversion/tmp/faceDetection/weakClassifier.html#determineAndSetClassifierWeight(double)http://localhost/var/www/apps/conversion/tmp/faceDetection/weakClassifier.html#getClassifierError()http://localhost/var/www/apps/conversion/tmp/faceDetection/weakClassifier.html#getClassifierFeature()http://localhost/var/www/apps/conversion/tmp/faceDetection/weakClassifier.html#getClassifierWeight()http://localhost/var/www/apps/conversion/tmp/faceDetection/weakClassifier.html#readWeakClassifier(java.lang.String)http://localhost/var/www/apps/conversion/tmp/faceDetection/weakClassifier.html#setClassifierError(double)http://localhost/var/www/apps/conversion/tmp/faceDetection/weakClassifier.html#setClassifierFeature(faceDetection.feature)http://localhost/var/www/apps/conversion/tmp/faceDetection/feature.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/feature.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/feature.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/weakClassifier.html#setClassifierFeature(faceDetection.feature)http://localhost/var/www/apps/conversion/tmp/faceDetection/weakClassifier.html#setClassifierError(double)http://localhost/var/www/apps/conversion/tmp/faceDetection/weakClassifier.html#readWeakClassifier(java.lang.String)http://localhost/var/www/apps/conversion/tmp/faceDetection/weakClassifier.html#getClassifierWeight()http://localhost/var/www/apps/conversion/tmp/faceDetection/weakClassifier.html#getClassifierFeature()http://localhost/var/www/apps/conversion/tmp/faceDetection/weakClassifier.html#getClassifierError()http://localhost/var/www/apps/conversion/tmp/faceDetection/weakClassifier.html#determineAndSetClassifierWeight(double)http://localhost/var/www/apps/conversion/tmp/faceDetection/trainImage.html#setWeight(double)http://localhost/var/www/apps/conversion/tmp/faceDetection/trainImage.html#setType(int)http://localhost/var/www/apps/conversion/tmp/faceDetection/trainImage.html#setIntegralImage(int[][])http://localhost/var/www/apps/conversion/tmp/faceDetection/trainImage.html#getWeight()http://localhost/var/www/apps/conversion/tmp/faceDetection/trainImage.html#getType()http://localhost/var/www/apps/conversion/tmp/faceDetection/trainImage.html#getIntegralImage()http://localhost/var/www/apps/conversion/tmp/icp/PGMImage.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/scanDetector.html#scanDetector(icp.PGMImage,%20byte[],%20int,%20int,%20int,%20int,%20int,%20int)http://localhost/var/www/apps/conversion/tmp/faceDetection/scanDetector.html#loadCascade()http://localhost/var/www/apps/conversion/tmp/faceDetection/detectionWindow.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/integrateMultipleDetections.html#printDetection(faceDetection.detectionWindow)


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This method sets the feature of a weak classifier.

weakClassifiertoString()This function prints the weak classifier to a string.

writeWeakClassifier(weakClassifierweakclassifier,

java.lang.String filename)

This function write the weakClassifier to a file.

Table 5-1: Inner details of the system classes.

5.2Pseudo Code

5.2.1Scan Detector

Recall that the detector gets scanned at all scales and location across the image, rather than

the image itself. This pseudo code scans the image at the base (initial) resolution of 19x19

at all locations in the image; it then scans the 320x240 resolution image. The scale of thedetector is increased and the process in repeated until its scanned the images at all scales.

windowWidth = 320 ;heightWidth = 240 ;

width = 19 ;height = 19 ;

for ( all possible scales a width*height sub-window can fit into windowWidth*heightWidth)

scanDetectorWithSubwindow(width, height ) ;

width ++ ;height ++ ;}

function scanDetectorWithSubwindow(width, height )

for (h = 0 to h < windowHeight )

for (w=0 to w < windowWidth )

if (w + width < windowWidth AND h + height < windowHeight )

classifySubWindow(w+width, h+height )

endIfendForendFor
http://localhost/var/www/apps/conversion/tmp/faceDetection/weakClassifier.html#weakClassifiertoString()http://localhost/var/www/apps/conversion/tmp/faceDetection/weakClassifier.html#writeWeakClassifier(faceDetection.weakClassifier,%20java.lang.String)http://localhost/var/www/apps/conversion/tmp/faceDetection/weakClassifier.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/weakClassifier.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/weakClassifier.htmlhttp://localhost/var/www/apps/conversion/tmp/faceDetection/weakClassifier.html#writeWeakClassifier(faceDetection.weakClassifier,%20java.lang.String)http://localhost/var/www/apps/conversion/tmp/faceDetection/weakClassifier.html#weakClassifiertoString()


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C H A P T E R 6

CODE DOCUMENTATION

The full code documentation of our code is not contained in this project due to the

number of pages it covers. The code documentation can however be found on the

accompanying compack disk(CD).

In the code documentation:

Every class and class method are described using in-line comments or a brief

detailed description of the algorithm or its workings. The same applies for

methods. The javadoc web pages makes for easily browsing the code

documentation. Where applicable, we note any caveatsthings that could go

wrong or things that the code doesnt address.


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C H A P T E R 7

TESTING DOCUMENT

This chapter describes how we tested our system. The system has been tested on the

MIT+CMU frontal face test set. The results of the face detector are shown below.

MIT+CMU frontal face test set. Images collected at CMU and MIT. 275 Correctly detected out of 472 images. 58% Detection rate

The following Figure 7.1 displays a false positive; the system detects the following sub-

window as a face, when its not.

Figure 7.1False positive detection


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C H A P T E R 8

USERS GUIDE

This document tells a user how to use the Access Control system. This document may

also be used by a programmer as a guide to improve or edit the system. This document

describes the system requirements to use the Access Control system, the system project

directory structure and how to run the Access Control system.

8.1 - System requirements

All the requirements to setup, run and edit the Access Control system are contained in

Table 8.1.

System requirements

Hardware Software

A personal computer (PC) that can satisfy

the software requirements and webcams

requirements.

Microsoft Windows based operating system

available from Microsoft Corporation.

A webcam. Installed Sun Java Runtime Environment

(JRE) and Java Development Kit (JDK)

6u2 (Update 2) available from Sun

Microsystems.

Installed Java Media Framework 2.1.1e

available from Sun Microsystems.

Installed NetBeans IDE 6.5 available from

www.netbeans.org

Table 8.1: The system requirements a table to setup, run andedit the Access Control system.


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8.2 - The Face Recognition System project directory

The Access Control System project directory is displayed in Figure 8.1 below. Table 8.2

explains the directory and its contents.

Figure 8.1: Access Control Systems project directory structure.


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Directory Contents/Description

Access_Control_System The main project directory which contains all

project directories. The directory contains two

files.AccessControl.jar an executable jar file that

runs the Access Control system.

AccessControlJavadocs.html a link to the

javadocs index.html file which documents the

source code.

Access_Control_System\UserGuide This directory contains the Html User Guide

file used by the Access Control system.

Access_Control_System\log This directory logs any Access Control activity.

The directory contains a directory namely

YEAR-MONTH-DAY, this directory contains

the log files for that day. The log files are

named per hour and minute the system was

terminated, e.g. HOUR-MINUTE.log

Access_Control_System\train This directory contains the training images used

for training the face detector, as well as theweak classifiers and cascade classifier the

detector uses for face detection.

Access_Control_System\dist\javadoc This directory contains the generated javadoc

files.

Access_Control_System\src The directory that contains all the source code

used by the Access Control system.

Access_Control_System\src\accessControl

System\resources

The directory that contains all the Access

Control resources.

Access_Control_System\src\faceDetection The source code directory for the faceDetection

package.


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Table 8.2: The Access Control system project directory andcontents explained.

8.3 - Running the Access Control system

Table 8.3 below describes the steps necessary to run the Access Control system.

Running the Access Control system

Step 1 Make sure that the system requirements in Table 8.1 are met.

Step 2 Make sure that a video capture device is connected to the PC.

Step 3 Double click the Executable Jar File named Access Control.jar to run theAccess Control System.

Table 8.3: Running the Access Control system.

8.4Complete user interface

Figure 8.2 below displays the main graphical user interface frame for our system titled

Access Control.


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Figure 8.2: Complete user interface

User Image panel

The User Image panel displays the user image of the user currently selected in the Userlist

panel.

User List panel

The User List panel lists the authorized users the Access Control system can recognize.

Search field

The search field can be used to search for a user in the face recognition system. This

feature can be very useful when the face recognition system have hundreds of users.

The search function searches for the substring of the search text in the username and

lists all the matched usernames in alphabetical order, displaying the first matched users

image in the user image panel.

Camera output

The camera output displays the cameras output. Used for capturing images when adding

users to the face recognition system, and for monitor current user login activity.


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Add user button

The add user button adds an authorized user to the Access Control system. On execution

of the Add User button the Add User dialog as shown in Figure 8.6 is displayed.

Remove user button

The remove button is only enabled when there are users in the system. The remove button

removes a user from the Access Control system. On execution of the Remove User

button the Confirm User Delete dialog in Figure 8.9 is displayed.

Recognition output

The Access Control system displays the output of the recognized user below camera

output. When a user is not recognized by the Access Control system, the system displays

Who are you? , otherwise the system displays the user ID of the recognized user and the

rate of the face recognition.

Log panel

When a user is detected by the face detection system and recognized by the face

recognition system, the Access Control system logs the users information to the log panel.

The log panel logs the user and the time at which the user logged in, the log file gets saves

with the current date every time the user terminates the system.

Acceptance threshold

The face recognition system has a threshold at which users should be recognized. With 0

being the lowest threshold value and with 1 being the highest threshold value. With 0

being the most lenient and 1 being the most strict at which the recognition operates. The

Acceptance threshold spinner is used to adjust the acceptance threshold value used with

which the ANNs output is compared[1].


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8.4.1 - How the user interface behaves

When the face detection system does not detect any faces or detects a false positive or the

face recognition system does not recognize the user. Then the system displays Who are

you?As displayed in Figure 8.4 below.


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Figure 8.4: How the user interface behaveswith detectionand no recognition.

When the users face is detected and recognized, the system will display the user ID and

the recognition rate as displayed in Figure 8.5 below.

Figure 8.5: How the user interface behaveswith detectionand recognition

8.4.1 - The Add User Dialog

The Add User Dialog is displayed in Figure 8.5 below, this frame is displayed when the

Add User button in the Access Control GUI is pressed or when the Add User menu item


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from the File menu is selected. The location of the Add User Dialog is such that the

cameras output can be clearly viewed. Each interface component in the Add User Dialog

with its purpose or action is described in Table 8.1.

Figure 8.6: The Add User dialog.

Add User dialog


User Image panel Displays the captured image.

Capture Image button Captures an image.

ID label and text field The text field where a user identifier should

be entered.

Ok button Add the user with the specified identifierthat should be recognized. If the ID text

field is empty the error dialog in Figure 8.7

is displayed. If no images was captured the

error dialog in Figure 8.8 is displayed.

Cancel button Do not add the user, discard the captured

image and close the Add User dialog.

Table 8.1: The Add User dialog interface componentsdescribed.


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From the add user dialog, if a user clicks Ok without entering a user ID or capturing an

image. The following dialog in Figure 8.7 below is displayed.

Figure 8.7: Error dialog displayed for empty ID text field or nocaptured image.

From the add user dialog, if a user clicks Ok without capturing an image. The following

dialog in Figure 8.8 below is displayed.

Figure 8.8: Error dialog displayed when no image wascaptured.


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8.4.2 - The Confirm User Delete dialog

The Confirm User dialog is displayed in Figure 8.9 below, this dialog is displayed when

selecting a user in the User List panel and clicking the Remove User button. When

removing a user from the system, the default option is set to no, such that the

administrator doesnt blindly remove a user from the Access Control system.

Figure 8.9: Confirm User Delete dialog displayed when clickingthe remove user button.

Confirm User Delete dialog


Yes button Completely removes the user from the

Access Control system.

No button Do not remove the user, close the

confirmation dialog.

Table 8.2: The Confirm User Delete dialog interfacecomponents described.


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8.4.3 - The File menu

The complete File menu with the menu items it contains is displayed in Figure 8.10 below.

Each menu item of the File menu with its associated action is described in Table 8.8.

Figure 8.10: The File menu.

The File menu

Menu item Action

Add User Adds a user to the Access Control system.Exit Exits the Access Control system.

Table 8.8: The File menu items described.


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8.4.4 - The Face Detection menu

The complete Face Detection menu with the menu items it contains is displayed in Figure

8.11 below. Each menu item of the Face Detection menu with its associated action is

described in Table 8.4.

Figure 8.11: The File Detection menu.

The Face Detection menu

Menu item Action

Settings Opens the Face Detection Settings dialog as

displayed in Figure 8.12.Table 8.4: The File menu items described.


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8.4.5Face Detection Settings dialog

The Face Detection Settings dialog is displayed in Figure 8.12 below, this dialog is

displayed when the Settings menu item from the Face Detection menu is selected.

In this dialog the administrator can set the scales in which the face detection shouldoperate. The face detection system will try and locate faces at the starting scale and

increasing in step size until it reaches the final scale. The default settings has a starting

scale of 19, thus the face detection system will try and locate all 19x19 faces in the camera

output. Then the system will increase this resolution by its step size, which are 5 by

default. The system then tries and locates all 24x24 faces in the camera output. The system

will continue increasing in step size until it reaches the final scale, which are set to 240x240

by default.

Choosing a big starting scale will dramatically improve the performance of the face

detection system and thus the Access Control system. This is because there are more small

scales in an 820x240 window, than big scales.

Important

These settings highly affect the performance of the Access Control system. The more

scale the face detections system has to cover, the slower the system will run.

The starting scale and final scale is also the distance as to how close a user should stand

in front of the camera in order to be detected by the face detection system; so much care

should be taken into playing with these settings.

Figure 8.12: The File Detection Setting dialog.


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8.4.6The Help menu

The complete Help menu with the menu items it contains is displayed in Figure 8.13. Each

menu item of the Help menu with its associated action is described in Table 8.5.

Figure 8.13: The Help menu.

The Help menu

Menu item Action

User Guide Open the Users Guide dialog displayed

in Figure 8.14.

About Open the About dialog displayed in

Figure 8.15.

Table 8.5: The Help menu items described.


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8.4.7The User Guide dialog

The Users Guide dialog is a modal dialog that is displayed in Figure 8.14. This dialog

contains the Users Guide for the Administrator. It is opened when the Users Guide menu

item from the Help menu is selected. The Users Guide dialog can be closed by either the

close button at the top right corner or the Ok button at the bottom of the dialog[1].

Figure 8.14: The Users Guide dialog.


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8.4.8The About dialog

The About dialog is a modal dialog that is displayed in Figure 8.15. This dialog contains a

message about the Access Control system. The About dialog can be closed by either the

close button at the top right corner or the OK button at the bottom of the dialog.

Figure 8.15: The About dialog.


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Figure 8.16 below displays the webcam that Users interact with and that is also used to

capture face images.

Figure 8.16: The webcam that Users interact with and that isalso used to capture face images.


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C h a p t e r 9

CONCLUSION

In this mini-theses, We discussed the implementation of a face detection system to be

used for access control. The focus was thus to implement a face detection system good

enough to be used for access control. Access control systems normally use video cameras

that deliver image data of poor quality and that also contain much noise [3]. The focus was

also on developing a real-time face detection system. The Viola and Jones have been most

suited for our requirements, thus we implemented a detector strongly based on the Viola

and Jones detector.


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BIBLIOGRAPHY

[1] A. Jorgensen. AdaBoost and Histograms for Fast Face Detection, 2006.

[2] B.K.L. Erik Hjelmas, Face Detection: A Survey, Computer Vision and Image

Understanding, vol. 3, no. 3, pp. 236-274, Sept. 2001.

[3] D. van Wyk .http://www.cs.uwc.ac.za/index.php/Honours-2006/Desmond-Van-

Wyk.html[online], November 2006

[4] J. Meynet. Fast Face Detection Using AdaBoost, July 2003.

[5] R. Lienhart. and J. Maydt., An extended set of Haar-like features for rapid object

detection. In: IEEE ICIP 2002, Vol.1, pp 900-903.

[6] P. Viola and M. Jones. Rapid object detection using a boosted cascade of

simple features. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, pages

511518, Dec 2001.

[7] P. Viola and M. Jones. Robust real-time object detection. IEEE ICCV Workshop

Statistical and Computational Theories of Vision, July 2001.

[8] Y. Freund and R.E. Schapire. A decision-theoretic generalization of on-line learningand an application to boosting. In Proceedings of the Second Europen Conference on Computational

Learning Theory, pages 2337. Springer-Verlag, 1995.
http://www.cs.uwc.ac.za/index.php/Honours-2006/Desmond-Van-Wyk.htmlhttp://www.cs.uwc.ac.za/index.php/Honours-2006/Desmond-Van-Wyk.htmlhttp://www.cs.uwc.ac.za/index.php/Honours-2006/Desmond-Van-Wyk.htmlhttp://www.cs.uwc.ac.za/index.php/Honours-2006/Desmond-Van-Wyk.html

dmitri warren de klerk - 2653786 - thesis

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