6.report face recognition

7/28/2019 6.Report Face Recognition

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A Matlab based Face Recognition using PCA 2013

Dept. of ECE, BGSIT Page 1

CHAPTER 1

INTRODUCTION

1.1 OBJECTIVES

Our project is mainly concerned with developing a Face Recognition system using

Eigenface recognition Algorithm. The Eigenface recognition is one of the mostsuccessful

techniques that have been used in image recognition and compression.

1.2 FACE RECOGNITION

Computerized human face recognition has been an active research area for the last

20 years. It has many practical applications, such as bankcard identification, aces control,mug shots searching, security monitoring, and surveillance systems. Face recognition is

used to identify one or more persons from still images or a video image sequence of a

scene by comparing input images with faces stored in a database. It is a biometric system

that employs automated methods to verify or recognize the identity of person based on

his/her physiological characteristic. In general, a biometric identification system makes

use of either physiological characteristics or behavior patterns to identify a person.

Because of human inherent protectiveness of his/her eyes, some people are reluctant to

use eye identification systems. Face recognition has the benefit of being a passive,

nonintrusive system to verify personal identity in a natural and friendly way.

The face is our primary focus of attention in social intercourse, playing a major

role in conveying identity and emotion. Hence face recognition has become an important

issue in many applications such as security systems, credit card verification and criminal

identification. Face Recognition is an emerging field of research with many challenges

such as large set of images, improper illuminating conditions. Much of the work in facerecognition by computers has focused on detecting individual features such as the eyes,

nose, mouth and head outline, and defining aface model by the position, size, and

relationships among these features. Such approaches have proven to depend on the

precise features.

Computational models of face recognition are interesting because they can

contribute not only to theoretical knowledge but also to practical applications.

Unfortunately, developing a computational model of face detection and recognition is


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quite difficult because faces are complex, multidimensional and meaningful visual

stimuli. The user should focus his attention toward developing a sort of early, pre

attentive Pattern recognition capability that does not depend on having three-dimensional

information or detailed geometry. He should develop a computational model of face

recognition that is fast, reasonably simple, and accurate.

Eigenface approach is one of the simplest and most efficient methods that can

locate and track a subject's head, and then recognize the person by comparing

characteristics of the face to those of known individuals. This approach treats the face

recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather

than requiring recovery of three-dimensional geometry, taking advantage of the fact that

faces are normally upright and thus may be described by a small set of 2-D characteristic

views. In eigenface approach, after the dimensional reduction of the face space, the

distance is measured between two images for recognition. If the distance is lessthan some

threshold value, then it is considered as a known face else it is an unknown face.Face

recognition is a very high level computer vision task, in which many early vision

techniques can be involved. Face Recognition can be divided into two parts.

1.3 FACE DETECTION

Face detection is largely motivated by the need for surveillance and security,

humancomputerintelligent interaction. Detecting faces is challenging due to the wide

varietiesof face appearances and the complexity of the backgrounds. The methods

proposed forface detection so far generally fall into two major categories: feature-based

methods andclassification-based approaches.

1.3.1 FEATURE BASED METHODS

These methods detect faces by searching for facial features andgrouping them into

faces according to their geometrical relationships. Since theperformance of feature-based

methods primarily depends on the reliable locationof facial features, it is susceptible to

partial occlusion, excessive deformation, andlow quality of images.

1.3.2 CLASSIFICATION BASED METHODS

These methods have used the intensity values of images as theinput features of the

underlying classifier. The Gabor filter banks, whose kernels are similar to the 2D


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receptive field profiles of the mammalian cortical simplecells, exhibit desirable

characteristics of spatial locality and orientation selectivity.As a result, the Gabor filter

features extracted from face images should be robustto variations due to illumination and

facial expression changes. We propose aclassification-based approach using Gabor filter

features for detecting faces.

1.4 FACE IDENTIFICATION

The first step of human face identification is to extract the relevant features from

facial images. Research in the field primarily intends to generate sufficiently reasonable

familiarities of human faces so that another human can correctly identify the face. The

question naturally arises as to how well facial features can be quantized. If such a

quantization if possible then a computer should be capable of recognizing a face given aset of features. Investigations by numerous researchers over the past several years have

indicated that certain facial characteristics are used by human beings to identify faces.

There are three major research groups which propose three different approaches to

the face recognition problem. The largest group has dealt with facial characteristics which

are used by human beings in recognizing individual faces. The second group performs

human face identification based on feature vectors extracted from profile silhouettes. The

third group uses feature vectors extracted from a frontal view of the face. Although there

are three different approaches to the face recognition problem, there are two basic

methods from which these three different approaches arise.

The first method is based on extracting feature vectors from the basic parts of a

face such as eyes, nose, mouth, and chin. In this method, with the help of deformable

templates and extensive mathematics, key information from the basic parts of a face is

gathered and then converted into a feature vector. L. Yullie and S. Cohen played a greatrole in adapting deformable templates to contour extraction of face images.

The second method is based on the information theory concepts, in other words,

on the principal component analysis methods. In this approach, the most relevant

information that best describes a face is derived from the entire face image. Based on the

Karhunen- Loeve expansions in pattern recognition, M. Kirby and L. Sirovich have

shown that any particular face could be economically represented in terms of a best

coordinate system that they termed "eigenfaces". These are the eigenfunctions of the


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averaged covariance of the ensemble of faces. Later, M. Turk and A. Pentland have

proposed a face recognition method based on the eigenfaces approach.

1.4.1 PRINCIPAL COMPONENT ANALYSIS METHOD

We have focused our research toward developing a sort of unsupervised patternrecognition scheme that does not depend on excessive geometry and computations like

Elastic bunch templates. Eigenfaces approach seemed to be an adequate method to be

used in face recognition due to its simplicity, speed and learning capability.

A previous work based on the eigenfaces approach was done by M. Turk and A.

Pentland, in which, faces were first detected and then identified. In this thesis, a facerecognition system based on the eigenfaces approach, similar to the one presented by M.

Turk and A. Pentland, is proposed. The scheme is based on an information theory

approach that decomposes face images into a small set of characteristic feature images

called eigenfaces, which may be thought of as the principal components of the initial

training set of face images. Recognition is performed by projecting a new image onto the

subspace spanned by the eigenfaces and then classifying the face by comparing its

position in the face space with the positions of known individuals.

Actual system is capable of both recognizing known individuals and learning to

recognize new face images. The eigenface approach used in this scheme has advantages

over other face recognition methods in its speed, simplicity, learning capability and

robustness to small changes in the face image.


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CHAPTER 2

THEORETICAL OVERVIEW

2.1 INTRODUCTION

Face recognition is a pattern recognition task performed specifically on faces. It

can be described as classifying a face either "known" or "unknown", after comparing it

with stored known individuals. It is also desirable to have a system that has the ability of

learning to recognize unknown faces.

Computational models of face recognition must address several difficult problems.

This difficulty arises from the fact that faces must be represented in a way that bestutilizes the available face information to distinguish a particular face from all other faces.

Faces pose a particularly difficult problem in this respect because all faces are similar to

one another in that they contain the same set of features such as eyes, nose, mouth

arranged in roughly the same manner.

2.2 BACKGROUND AND RELATED WORK

Much of the work in computer recognition of faces has focused on detectingindividual features such as the eyes, nose, mouth, and head outline, and defining a face

model by the position, size, and relationships among these features. Such approaches

have proven difficult to extend to multiple views and have often been quite fragile,

requiring a good initial guess to guide them. Research in human strategies of face

recognition, moreover, has shown that individual features and their immediate

relationships comprise an insufficient representation to account for the performance of

adult human face identification. Nonetheless, this approach to face recognition remainsthe most popular one in the computer vision literature.

Bledsoe was the first to attempt semi-automated face recognition with a hybrid

human-computer system that classified faces on the basis of fiducial marks entered on

photographs by hand. Parameters for the classification were normalized distances and

ratios among points such as eye corners, mouth corners, nose tip, and chin point. Later

work at Bell Labs developed a vector of up to 21 features, and recognized faces using

standard pattern classification techniques.


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Fischler and Elschlager attempted to measure similar features automatically. They

described a linear embedding algorithm that used local feature template matching and a

global measure of fit to find and measure facial features. This template matching

approach has been continued and improved by the recent work of Yuille and Cohen.

Their strategy is based on deformable templates, which are parameterized models of theface and its features in which the parameter values are determined by interactions with the

face image.

Connectionist approaches to face identification seek to capture the configurational

nature of the task. Kohonen and Kononen and Lehtio describe an associative network

with a simple learning algorithm that can recognize face images and recall a face image

from an incomplete or noisy version input to the network. Fleming and Cottrell extend

these ideas using nonlinear units, training the system by back propagation.

Others have approached automated face recognition by characterizing a face by a

set of geometric parameters and performing pattern recognition based on the parameters.

Kanade's face identification system was the first system in which all steps of the

recognition process were automated, using a top-down control strategy directed by a

generic model of expected feature characteristics. His system calculated a set of facial

parameters from a single face image and used a pattern classification technique to match

the face from a known set, a purely statistical approach depending primarily on local

histogram analysis and absolute gray-scale values.

Recent work by Burt uses a smart sensing approach based on multiresolution

template matching. This coarse to fine strategy uses a special purpose computer built to

calculate multiresolution pyramid images quickly, and has been demonstrated identifying

people in near real time.

2.3FACE RECOGNITION WITH EIGENFACES

It is the most thoroughly researched among these. According to Sirovich and

Kirby [1] principal component or eigenvectors can be used to represent face images.

Their research showed that a face could be approximately reconstructed by a collection of

weights describing each face, and a standard eigenpicture. The weights of each face are

obtained by projecting the test image on the eigenpicture. Turk and Pentland [2] used this


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idea of eigenface for face detection and identification. Mathematically eigenfaces are the

principal components of the distribution of faces or the eigenvectors of the covariance

matrix of the set of face images (this is explained in chapter 2). The eigenvectors account

for the variation between the images in a set. A face can be represented by a linear

combination of the eigenvectors. Later it was shown thatillumination normalization [6] isrequired for the eigenface approach.

Pentland et al [3] used the eigenface recognition technique and extended it to

facial features such as eyes, nose, and mouth which can be called eigenfeatures. This

modular eigenspace approach comprising of the eigenfeatures namely eigeneyes,

eigennose and eigenmouth was found to be less sensitive to changes in appearance than

the standard eigenface method. To summarize eigenface method is not invariant to

illumination and changes in scale.

2.4 NEURAL NETWORK

As mentioned previously is another approach in face recognition. The difference

from eigenface method lies in the fact that it is nonlinear. Due to the non linear nature of

the network the feature extraction is more efficient than eigenface. WISARD ( Wilkie,

Stonham and Aleksanders Recognition Device ) was one of the first artificial neural

network (ANN) schemes. It was a single layer adaptive network and each individual [4]stored in it had a separate network. Neural network is application oriented. For face

detection multilayer perceptron and convolutional neural network [12] has been applied.

Again for face verification Cresceptron [5] which is a multiresolution pyramidal structure

is used. The Convolutional network provides partial invariance to translation, rotation,

scale, and deformation. Lins [6] probabilistic decision -based neural network (PDBNN)

has a modular structure and a decision based neural network. PDBNN can be applied to

face detection, eye localization and face recognition. PDBNN has the merits of bothneural networks and statistical approaches and its distributed computing principle is

relatively easy to implement on a parallel computer. However when the number of people

increases, the need for more computation also increases. This is because the training time

increases. Also, it is not suitable for single model image recognition as multiple model

images per person are required for the optimal training of the system.


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2.5 DYNAMIC LINK ARCHITECTURE

It is another method for face recognition. It is an extension of the classical

artificial neural network. In this method, memorized objects are represented by sparse

graphs, whose vertices are labeled with a multi-resolution description in terms of a local

power spectrum and whose edges are labeled with geometrical distance vectors. However in this method the matching process is computationally expensive. Wiskott and Von der

Malsburg extended the technique and matched faces against a

gallery of 112 neutral frontal view faces. They reported 86.5 percent and 66.4 percent for

matching 111 faces of 15 degree and 110 faces of 30 degree rotation to a gallery of 112

neutral frontal faces. In general, dynamic link architecture is superior to other face

recognition techniques in terms of rotation invariance but the matching process iscomputationally expensive.

2.6 HIDDEN MARKOV MODEL

Another approach to face recognition is Hidden Markov Model . Samaria and

Fallside [8] applied this method to human face recognition. Faces were divided into

regions like the eyes, nose, mouth which could be associated with the states of the hidden

Markov model. Since HMM requires 1 dimensional observation sequence and the imagesare in 2D , the images must be converted to either 1D temporal sequences or 1D spatial

sequences . An unknown test image is first sampled to an observation sequence. Then it is

matched against every HMMs in the model face database. The match with the hig hest

likelihood is considered the best match and the relevant model reveals the identity of the

test face. The recognition rate is 87 percent using ORL database. The training time is

usually high for this approach.

2.7 GEOMETRICAL FEATURE MATCHING TECHNIQUE

It is based on the computation of the geometrical features of the face. The overall

configuration is described by a vector representing the position and size of the facial

features like eyes, nose, mouth and shape of the outline of the face. Kanades [ 9] face

recognition based on this approach reached a 75 percent recognition rate. Brunelis and

Poggios [10] method extracted 35 features from the face to form a 35 dimensional

vector. The recognition was performed with a Bayes classifier. They reported a


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recognition rate of 90 percent on a database of 47 people. In these methods, typically 35

45 feature points per face were generated. Geometrical feature matching depends on

precisely measured distances between features, which in turn depend on the accuracy of

the feature location algorithms.

2.8TEMPLATE MATCHING

In this method a test image is represented as a two-dimensional array of intensity

values and is compared using a metric such as Euclidean distance with a single template

representing the whole face. In this method, each viewpoint of an individual can be used

to generate a template. Thus, multiple templates can represent each person in the

database. A single template corresponding to a viewpoint can be made up of smaller distinctive templates [24] [10]. Bruneli and Poggio [10] extracted four features namely

the eyes, nose, mouth and the entire face and compared the performance with their

geometrical method with the template method. The template matching method was found

to be superior to the geometrical matching technique. Drawbacks of template matching

include the complexity of the computation and the description of the templates. As the

recognition system is tolerant to certain discrepancies between the test image and the

template, the tolerance might average out the differences that make a face unique.

2.9 THE ACQUISITION MODULE

This is the entry point of the face recognition process. It is the module where the

faceimage under consideration is presented to the system. An acquisitionmodule can

request a face image from several different environments: The face imagecan be an image

file that is located on a magnetic disk, it can be captured by a framegrabber or it can be

scanned from paper with the help of a scanner.


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2.10 OUTLINE OF A TYPICAL FACE RECOGNITION SYSTEM

Vector Face Image Normalized face Image Feature

Classified known or unknown

Figure 2.1: Outline of a Face Recognition System

There are six main functional blocks, whose responsibilities are given below:

2.11 THE PRE-PROCESSING MODULE

In this module, by means of early vision techniques, face images are normalized

and if desired, they are enhanced to improve the recognition performance of the system.

Some or all of the following pre-processing steps may be implemented in a face

recognition system:

2.11.1 IMAGE SIZE NORMALIZATION

It is usually done to change the acquired image size to a default image size such

as 128 x 128, on which the face recognition system operates. This is mostly encountered

in systems where face images are treated as a whole like the one proposed in this thesis.

2.11.2 HISTOGRAM EQUALIZATION

It is usually done on too dark or too bright images in order to enhance image

quality and to improve face recognition performance. It modifies the dynamic range

(contrast range) of the image and as a result, some important facial features become more

apparent.


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Histogram equalization is applied in order to improve the contrast of the images.

The peaks in the image histogram, indicating the commonly used grey levels, are

widened, while the valleys are compressed.

Figure 2.2: Histogram Equalization

2.11.3 Median Filtering

For noisy images especially obtained from a camera or from aframe grabber,

median filtering can clean the image without losing information .

2.11.4 High Pass Filtering

Feature extractors that are based on facial outlines, may benefit the results that are

obtained from an edge detection scheme. High-pass filtering emphasizes the details of an

image such as contours which can dramatically improve edge detection performance.

2.11.5 Background Removal

In order to deal primarily with facial information itself, face background can be

removed. This is especially important for face recognition systems where entire

information contained in the image is used.


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2.11.6 Rotation Correction

When the locations of eyes, nose and mouth are given for a face image, this data

can be used to rotate the image so that all the face images are exactly positioned the same.

When the positions for the right and left eyes are known, the inverse tangent of the angle

between the lines, l1 and l2, connecting the mid-points of two eyes can be calculated. The

image can be rotated using the calculated angle. This process is drawn in Figure 2.9.6.

Figure 2.3: The rotation correction procedure when eye, nose and mouth locations are given.

2.11.7 Masking

By using a mask, which simply has a face shaped region, the effect ofbackground

change is minimized. The effect of masking is studied only onFERET database images.

The mask used in this study is shown in Figure 2.4.

Figure 2.4: The face shaped mask


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Figure 2.5: Masking

2.11.8 Translational and Rotational Normalizations

In some cases, it is possible to work on a face image in which the head is

somehow shifted or rotated. Especially for face recognition systems that are based on the

frontal views of faces, it may be desirable that the pre-processing module determines and

if possible, normalizes the shifts and rotations in the head position .

2.11.9 Illumination Normalization

Face images taken under different illuminations can degrade recognition

performance especially for face recognition systems based on the principal component

analysis in which entire face information is used for recognition. A picture can be

equivalently viewed as an array of reflectivities r(x).

Thus, under a uniform illumination I, the corresponding picture is given by

The normalization comes in imposing a fixed level of illumination I0 at a

reference point x 0 on a picture. The normalized picture is given by

In actual practice, the average of two reference points, such as one under each eye,

each consisting of 2 x 2 array of pixels can be used.


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2.12 THE FEATURE EXTRACTION MODULE

After performing some pre-processing (if necessary), the normalized face image is

presented to the feature extraction module in order to find the key features that are going

to be used for classification. In other words, this module is responsible for composing a

feature vector that is well enough to represent the face image.

2.13 THE CLASSIFICATION MODULE

In this module, with the help of a pattern classifier, extracted features of the face

image is compared with the ones stored in a face library (or face database). After doing

this comparison, face image is classified as either known or unknown .

2.14 TRAINING SET

Training sets are used during the "learning phase" of the face recognition process.

The feature extraction and the classification modules adjust their parameters in order to

achieve optimum recognition performance by making use of training sets.

2.15 FACE LIBRARY OR FACE DATABASE

After being classified as "unknown", face imagescan be added to a library (or to a

database) with their feature vectors for latercomparisons. The classification module

makes direct use of the face library.


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CHAPTER 3

FACE RECOGNITION USING PRINCIPAL

COMPONENTANALYSIS

3.1 INTRODUCTION

Principal component analysis (PCA) was invented in 1901 by Karl Pearson.The

Principal Component Analysis (PCA) is one of the most successful techniques that have

been used in image recognition and compression. PCA is a statistical method under the

broad title off actor analysis. The purpose of PCA is to reduce the large dimensionality of

the data space (observed variables) to the smaller intrinsic dimensionality of feature space(independent variables), which are needed to describe the data economically. This is the

case when there is a strong correlation between observed variables. The jobs which PCA

can do are prediction, redundancy removal, feature extraction, data compression, etc.

Because PCA is a classical technique which can do something in the linear domain,

applications having linear models are suitable, such as signal processing, image

processing, system and control theory, communications, etc. Depending on the field of

application, it is also named the discrete Karhunen Love transform (KLT), the Hotelling

transform or proper orthogonal decomposition (POD).

Face recognition has many applicable areas. Moreover, it can be categorized into

face identification, face classification, or sex determination. The most useful applications

contain crowd surveillance, video content indexing, personal identification (ex:

driverslicence), entrance security, etc. The main idea of using PCA for face recognition

is to express the large 1-D vector of pixels constructed from 2-D facial image in to the

compact principal components of the feature space. This can be called eigenspace

projection. Eigenspace is calculated by identifying the eigenvectors of the covariance

matrix derived from set official images (vectors).

3.2 OUTLINE OF THE PROPOSED FACE RECOGNITION SYSTEM

The proposed face recognition system passes through three main phases during a

facerecognition process. Three major functional units are involved in these phases and


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they are depicted in Figure 3.1. The characteristics of these phases in conjunction with

thethree functional units are given below:

Figure 3.1: Functional block diagram of the proposed face recognition system

3.3 FACE LIBRARY FORMATION PHASE

In this phase, the acquisition and the pre-processing of the face images that are

going to be added to the face library are performed. Face images are stored in a face

library in the system. We call this face database a "face library" because at the moment, it

does not have the properties of a relational database. Every action such as training set or

eigenface formation is performed on this face library. Face library is

initially empty. In order to start the face recognition process, this initially emptyface library has to be filled with face images. The proposed face recognition system

operates on image files of any resolution. In order to perform image size conversions and

enhancements on face images, there exists the "pre-processing" module. This module

automatically converts every face image to 200 x 180 (if necessary) and based on user

request, it can modify the dynamic range of face images (histogram equalization) in order

to improve face recognition performance. After acquisition and pre-processing, face

image under consideration is added to the face library. Each face is represented by two

entries in the face library: One entry corresponds to the face image itself (for the sake of


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speed, no datacompression is performed on the face image that is stored in the face

library) and the other corresponds to the weight vector associated for that face image.

Weight vectors of the face library members are empty until a training set is chosen and

eigenfaces are formed.

3.4 TRAINING PHASE

After adding face images to the initially empty face library, the system is ready

toperform training set and eigenface formations. Those face images that are going to be

inthe training set are chosen from the entire face library. Now that the face library entries

are normalized, no further pre-processing is necessary at this step. After choosingthe

training set, eigenfaces are formed and stored for later use. Eigenfaces are calculatedfrom

the training set, keeping only the M images that correspond to the highest eigenvalues.These M eigenfaces define the M-dimensional "face space". As new faces are

experienced, the eigenfaces can be updated or recalculated. The corresponding

distribution in the M-dimensional weight space is calculated for each face library

member, by projecting its face image onto the "face space" spanned by the eigenfaces.

Now the corresponding weight vector of each face library member has been updated

which were initially empty. The system is now

ready for the recognition process. Once a training set has been chosen, it is not

possible to add new members to the face library with the conventional method that is

presented in "phase 1" because, the system does not know whether this item already

exists in the face library or not. A library search must be performed.

3.5 RECOGNITION AND LEARNING PHASE

After choosing a training set and constructing the weight vectors of face library

members, now the system is ready to perform the recognition process. User initiates the

recognition process by choosing a face image. Based on the user request and the acquired

image size, pre-processing steps are applied to normalize this acquired image to face

library specifications (if necessary). Once the image is normalized, its weight vector is

constructed with the help of the Eigen faces that were already stored during the training

phase. After obtaining the weight vector, it is compared with the weight vector of every

face library member within a user defined "threshold". If there exists at least one face

library member that is similar to the acquired image within that threshold then, the face


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image is classified as "known". Otherwise, a miss has occurred and the face image is

classified as "unknown".

3.6 MATHEMATICS OF PCA

A 2-D facial image can be represented as 1-D vector by concatenating each row

(or column) into a long thin vector. Lets suppose we have M vectors of size N (= rows of

image columns of image) representing a set of sampled images. p js represent the pixel

values.

The images are mean centered by subtracting the mean image from each image vector.Let m represent the mean image.

And let w i be defined as mean centered image.

Our goal is to find a set of e is which have the largest possible projection onto each of the

w is. We wish to find a set of M orthonormal vectors e i for which the quantity

is maximized with the orthonormality constraint

It has been shown that the e is and is are given by the eigenvectors and eigenvalues of

the covariance matrix.


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where W is a matrix composed of the column vectors wi placed side by side. The size of

C is N N which could be enormous. For example, images of size 64 64 create the

covariance matrix of size 40964096. It is not practical to solve for the eigenvectors of C

directly. A common theorem in linear algebra states that the vector s ei and scalars i can

be obtained by solving for the eigenvectors and eigenvalues of the M M matrix WT

W.Let d i and I be the eigenvectors and eigenvalues of W TW, respectively.

By multiplying left to both sides by W

which means that the first M-1 eigenvectors e i and Eigen values iof WW T are given by

W d i and i, respectively. W d i needs to be normalized in order to be equal to ei. Since we

only sum up a finite number of image vectors, M, the rank of the covariance matrix

cannot exceed M- 1 (The -1 comes from the subtraction of the mean vector m).

The eigenvectors corresponding to non-zero eigenvalues of the covariance matrix

produce an orthonormal basis for the subspace within which most image data can be

represented with a small amount of error. The eigenvectors are sorted from high to low

according to their corresponding eigenvalues. The eigenvector associated with the largest

eigenvalue is one that reflects the greatest variance in the image. That is, the smallest

eigenvalue is associated with the eigenvector that finds the least variance. They decrease

in exponential fashion, meaning that the roughly 90% of the total variance is contained in

the first 5% to 10% of the dimensions.

A facial image can be projected onto M (


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the eigenfaces as a basis set for facial images. The simplest method for determining

which face class provides the best description of a n input facial image is to find the face

class k that minimizes the Euclidean distance

where k is a vector describing the k th face class. If k is less than some predefined

threshold , a face is classified as belonging to the class k.

3.7 USING EIGENFACES TO CLASSIFY A FACE IMAGE

The eigenface images calculated from the eigenvectors of L span a basis set with

whichto describe face images. Sirovich and Kirby evaluated a limited version of this

frameworkon an ensemble of M = 115 images of Caucasian males digitized in a

controlled manner,and found that 40 eigenfaces were sufficient for a very good

description of face images.

In practice, a smaller M' can be sufficient for identification, since accurate

reconstruction of the image is not a requirement. Based on this idea, the proposed face

recognition system lets the user specify the number of eigenfaces (M') that is going to be

used in the recognition. For maximum accuracy, the number of eigenfaces should beequal to the number of images in the training set. But, it was observed that, for a training

set of fourteen face images, seven eigenfaces were enough for a sufficient description of

the training set members.

In this framework, identification becomes a pattern recognition task. The

eigenfaces span an M' dimensional subspace of the original N 2 image space. The M'

significant eigenvectors of the L matrix are chosen as those with the largest associated

eigenvalues.

A new face image () is transformed into its eigenface components (projected

onto "face space") by a simple operation,

for k = 1,...,M'.


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The weights form a feature vector ,

that describes the contribution of each eigenface in representing the input face

image,treating the eigenfaces as a basis set for face images. The feature vector is then

used in astandard pattern recognition algorithm to find which of a number of predefined

faceclasses, if any, best describes the face. The face classesican be calculated by

averagingthe results of the eigenface representation over a small number of face images

(as few asone) of each individual. In the proposed face recognition system, face

classescontainonly one representation of each individual.

Classification is performed by comparing the feature vectors of the face library

members with the feature vector of the input face image. This comparison is based on the

Euclidean distance between the two members to be smaller than a user defined threshold

k . If the comparison falls within the user defined threshold, then face image is classified

as known, otherwise it is classified as unknown.

3.8 REBUILDING A FACE IMAGE WITH EIGENFACES

A face image can be approximately reconstructed (rebuilt) by using its feature

vector andtheeigenfaces as

where

is the projected image.


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The face image under consideration is rebuilt just by adding each eigenface with a

contribution of w ito the average of the training set images. The degree of the fit or the

"rebuild error ratio" can be expressedby means of the Euclidean distance between the

original and the reconstructed face image.

It has been observed that, rebuild error ratio increases as the training set members differ

heavily from each other. This is due to the addition of the average face image. When the

members differ from each other (especially in image background) the average face image

becomes messier and this increases the rebuild error ratio.

There are four possibilities for an input image and its pattern vector:

Near face space and near a face class,

Near face space but not near a known face class,

Distant from face space and near a face class,

Distant from face space and not near a known face class.

In the first case, an individual is recognized and identified. In the second case, an

unknown individual is presented. The last two cases indicate that the image is not a face

image. Case three typically shows up as a false classification. It is possible to avoid this

false classification in this system.

where k is a user defined threshold for the faceness of the input face images belonging

to k th face class.


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3.9 SUMMARY OF THE EIGENFACE RECOGNITION

PROCEDURE

The eigenfaces approach to face recognition can be summarized in the following steps:

Form a face library that consists of the face images of known individuals. Choose a training set that includes a number of images (M) for each person

with

some variation in expression and in the lighting.

Calculate the M x M matrix L, find its eigenvectors and eigenvalues, and

choose theM' eigenvectors with the highest associated eigenvalues.

Combine the normalized training set of images to produce M'eigenfaces. Store

these eigenfaces for later use.

For each member in the face library, compute and store a feature vector. Choose a threshold that defines the maximum allowabledistance from any

face class. Optionally choose a threshold that defines the maximum

allowable distance from face space.

For each new face image to be identified, calculate its feature vector and

compare it with the stored feature vectors of the face library members

Ifthe comparison satisfies the condition thenfor at least one member, then

classify this face image as "known", otherwise amiss has occurred and classify

it as "unknown".


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CHAPTER 4

PROJECT IMPLEMENTATION

4.1 FLOWCHART

Start

Image size reduction andgrayscale conversion

Face Detection

Principal ComponentAnalysis, Finding Eigenfaces

Projected training images(image feature vectors)

Image size reduction andgrayscale conversion

Face Detection

Projected Test Image (test

image feature vector)

Compare?

Match No Match

Show the result

Stop

Acquire a trainingset of face images

Acquire testimage


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4.2 SOFTWARE REQUIREMENTS Matlab R2011b

4.3DATA ACQUISITION

Here a directory dialog box is opened up by using Matlab commands to select a

folder where the training set of images is stored. This dialog box helps to select any folder

thus overcoming the concept of hard coding where the user has to mention the particular

folder path. This dialog box is shown below:

Figure 4.1 Selecting training folder

4.4 TRAINING SET

The training set defines the subspace in which recognition will be performed.

Aftertraining, the recognition system develops an efficient seperability among the

trainingimages using the PCA technique. An assumption is made on the training set: it

canaccurately represent each image in the gallery, which means that the separability of

the system can also be well applied to the gallery. Training set size determines

thedimensionality of the subspace. Apparently, the more sparsely the gallery images

aredistributed, the higher separability the recognitionsystem can achieve. Generally,

byincreasing the dimensionality, the images should be more sparsely distributed.

Hence,there are two

schemes of selecting training sets to enhance recognition performance. Oneis to

make the training set more representative of the gallery; the other is to enlarge thetraining

set size in order to increase the dimensionality of the subspace.

Let the training set of the image be 1, 2, 3.. n.


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Figure 4.2 Training Set of Image

4.5 Pre Processing Techniques

4.5.1 Image Size Reduction

The images read one by one are reduced into size of 200180 so as to increase the

speed of processing and also to get rid of software hang and errors.

4.5.2Grayscale Conversion

Then all input images are converted into grayscale for the purpose of

normalization and error reduction.


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Fi

gure4.3: Grayscale conversion of all images

4.5.3 Normalized Training Set

In image processing, normalization is a process that changes the range of pixel intensity

values. Applications include photographs with poor contrast due to glare, for example.

Normalization is sometimes called contrast stretching. In more general fields of data

processing, such as digital signal processing, it is referred to as dynamic range expansion.

The purpose of dynamic range expansion in the various applications is usually to bring

the image, or other type of signal, into a range that is more familiar or normal to the

senses, hence the term normalization. Often, the motivation is to achieve consistency in

dynamic range for a set of data, signals, or images to avoid mental distraction or fatigue.

For example, a newspaper will strive to make all of the images in an issue share a similar

range of grayscale.

For example, if the intensity range of the image is 50 to 180 and the desired range is 0 to

255 the process entails subtracting 50 from each of pixel intensity, making the range 0 to

130. Then each pixel intensity is multiplied by 255/130, making the range 0 to 255.
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Figure 4.4: Normalized Training Set

4.6 MEAN

It is defined as the arithmetic average of the training image vectors at each pixel

point and its size is (N x 1).

The Mean face set is defined by

Subtract the Mean Face.

It gives us the difference of the training image from the mean image (size N x 1).


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Figure 4.5: Mean Image

4.7 COVARIANCE MATRIX

Definition: it is the measure of how much two random variables vary together (as

distinct from variance, which measures how much a single variable varies). If two

variables tend to vary together (that is, when one of them is above its expected value, then

the other variable tends to be above its expected value too), then the covariance between

the two variables will be positive. On the other hand, if when one of them is above its

expected value, the other variable tends to be below its expected value, then the

covariance between the two variables will be negative.

An important property of the Eigenface method is obtaining the eigenvectors of

the covariance matrix. For a face image of size (N x x N y) pixels, the covariance matrix is

of size (P x P), P being (N x x N y). This covariance matrix is very hard to work with due to

its huge dimension causing computational complexity. On the other hand, Eigenface

method calculates the eigenvectors of the (M t x M t) matrix, Mt being the number of face

images, and obtains (P x P) matrix using the eigenvectors of the (M t x M t) matrix. Thus, it


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is possible to obtain the eigenvectors of X by using the eigenvectors of Y. A matrix of

size (M t x M t) is utilized instead of a matrix of size (P x P) (i.e. [{N x x N y} x {N x x N y}]

). This formulation brings substantial computational efficiency.

4.8 EIGENVECTOR AND EIGENVALUE

Definition: Let A be an n x n matrix. Then a real number is called an eigenvalue

of the matrix, A if and only if, there is a n-dimensional nonzero vector, v for which

eigenvectors can be considered as the vectors pointing in the direction of the maximum

variance and the value of the

variance the eigenvector represents is directly proportional to the value of the

eigenvalue (i.e. the larger the eigenvalue indicates the larger variance the eigenvector

represents). Hence, the eigenvectors are sortedwith respect to their

correspondingeigenvalues. The eigenvector having the largest eigenvalue is marked as the

first eigenvector, and so on. In this manner, the most generalizing eigenvector comes first

in the eigenvector matrix.

Example: Eigenvectors

Eigenvalue

Eigenvectors


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Figure 4.6 Eigenvectors

Eigenvalues

Figure 4.7: Eigenvalues

4.9EIGENFACES

Eigenfaces are a set of eigenvectors used in the computer vision problem of

human face recognition. The approach of using eigenfaces for recognition was developed

by Matthew Turk and Alex Pentland beginning in 1987, and is considered the first facial

recognition technology that worked. These eigenvectors are derived from the covariance

matrix of the probability distribution of the high-dimensional vector space of possible

faces of human beings To generate a set of eigenfaces, a large set of digitized images of

human faces, taken under the same lighting conditions, are normalized to line up the eyes

and mouths. They are then all resampled at the same pixel resolution. Eigenfaces can be

extracted out of the image data by means of a mathematical tool called Principal

Component Analysis (PCA).


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Figure 4.8: Eigenfaces

4.10 SELECTING THE TEST IMAGE

The test image is again selected when a dialog box opens. From here, we can

select any file image from our hard disk without mentioning the image name and format

instantaneously

Figure4.9: Selecting Test Image

4.11 CLASSIFICATION

The process of classification of a new (unknown) face new to one of the classes

(known faces) can be carried out in two ways:

4.11.1 Bayesian classifier


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Bayesian classifier is one of the most widely applied statistical approaches in the

standard pattern classification. It assumes that the classification problem is posed in

probabilistic terms, and that all the relevant probability values are known. The decision

process in statistical pattern recognition can be explained as follows:

First, the new image is transformed into its eigenface components. The resulting

weights form the weight vector newT.

Figure 4.10: Face Space

4.11.2. Nearest Mean Classifier

Nearest Mean Classifier is an analogous approach to the Nearest Neighbor

Rule(NN-Rule). In the NN-Rule, after the classification system is trained by thesamples,

a test data is fed into the system and it is classified in the class of

thenearesttrainingsample in the data space with respect to Euclidean Distance. Inthe

Nearest Mean Classifier, again the Euclidean distance from each class mean(in this case)

is computed for the decision of the class of the test data. Inmathematical terms, the

Euclidean distance between the test sample x, and each face class mean i is:


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where x is a d dimensional input data .

After computing the distance to each class mean, the test data is classified into the

class with minimum Euclidean Distance.

4.12 WEIGHT AND EUCLIDEAN DISTANCE

Figure 4.11: Weight & Euclidean Distance

4.13Matlab Output

Based on the least value of the Euclidean distance, face recognition is carried out.


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Figure 4.12: Output (reconstructed image)

The above output is obtained when we select a different test image of the person

which is not present in the database of images. The algorithm finds the correct match.

CHAPTER 5

Face Detection

5.1 INTRODUCTION

Detecting human faces automatically is becoming a very important and

challenging task in computer vision research. The significance of the problem can be

easily illustrated by its vast applications, as face detection is the first step towards

intelligent vision-based human computer interaction. Face recognition, face tracking,

pose estimation and expression recognition all require robust face detecting algorithmsfor successful implementation. Segmenting facial regions in images or video sequences

can also lead to more efficient coding schemes [1], content-based representation

(MPEG4) [2], three-dimensional human face model fitting, image enhancement and

audio-visual speech integration [3]. Although a major area of interest, many problems

still need to be solved, as segmenting a human face successfully depends on many

parameters such as skin-tones under varying lighting conditions, complexity level of the

background in the image to be segmented and application for which the segmentation is


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required. Inherent differences due to the existence of different ethnic backgrounds,

gender and age groups also complicate the face detection paradigm.

With so many new applications, the development of faster and more robust face

detection algorithms has become a major area of research over the last few years.

Techniques based on knowledge of rules that capture the relationship between facial

features, feature invariant approaches that tend to define structural features that exist

even when the pose, viewpoint and lighting condition vary, and template matching

methods that use several standard templates to describe a face, are all being thoroughly

investigated. A comprehensive survey on the methods used to detect faces in images can

be found in [4]. However, many of the successful claims reported in the literature either use data sets that are too small or the test images acquired are not standard images and so

can show biased results favouring one method over another. The comparison difficulties

are due to the fact that much less attention has been paid to the development of a

standard face detection database that can be used as a benchmark to test the performance

of these new algorithms.

5.2 Definition and relation to other tasks

Face detection is a computer technology that determines the locations and sizes

of human faces in arbitrary (digital) images. It detects facial features and ignores anything

else, such as buildings, trees and bodies.

Face detection can be regarded as a specific case of object-class detection. In

object-class detection, the task is to find the locations and sizes of all objects in an image

that belong to a given class. Examples include upper torsos, pedestrians, and cars.

Face detection can be regarded as a more general case of face localization . In face

localization, the task is to find the locations and sizes of a known number of faces

(usually one). In face detection, one does not have this additional information.

Early face-detection algorithms focused on the detection of frontal human faces,

whereas newer algorithms attempt to solve the more general and difficult problem of

multi-view face detection. That is, the detection of faces that are either rotated along the

axis from the face to the observer (in-plane rotation), or rotated along the vertical or left-
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right axis (out-of-plane rotation), or both. The newer algorithms take into account

variations in the image or video by factors such as face appearance, lighting, and pose.

5.3 FACE DETECTION USING PCA

In our experiments we use a 10-dimensional principal subspace (M = 10). The

projection matrix PM is calculated by an eigenspace decomposition using a set of

luminance face test images. To localize a facial region in a new image, the error criterion

must be computed for each pixel position, resulting in a distance map. For the moment,

we use a simple detector which is based only on the \distance from face space" (DFFS).

The globalminimum of the distance map is then selected as the bestmatch. When using M

eigenvectors, the computation of the DFFS requires M+ 1 correlations (withthe M

\eigenfaces" and the mean image) and an additional energy calculation. The correlations;ongrey level images and point out the problems causedby the image background.

Fig. 5.1 shows the DFFS map for the luminance image

salesman#1. The global minimum is marked by the white circle. Though there is a local

minimum at the true face position, the best match is in the background region leading to a

false detection. The DFFS is high for non-facial image regions with a high changing in

the intensity (e.g. the area thatincludes parts of the light-colored shirt and the dark background). On the other hand, the DFFS becomes relative small in non-facial regions

with little variance in the intensity like parts of the background at the right side of the test

image. This is due to the fact that an image pattern that can be modeled by noise can be

better represented by the eigenfaces than a non-facial image pattern containing a strong

edge. Therefore, detection based on the DFFS becomes di_cult even in images with a

simple background if the face region does not cover the main part of the test image.We

now apply a principal component analysis to the skin probability image de_ned by

equation (2) using the same projection matrix. The DFFS map for the skin probability

image shown in _g. 2 is displayed in _g. 4a. The true face region is characterized by a

local minimum. Similar to the luminance case, the error criterion is also low in

background regions with little variance. These regions represent nonskin-colored

background, so the probability image isnear zero in these areas.


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5.1 Detected face region

In 5.1 the eigenspace decomposition is used to estimate the complete probability

density for a certain object class (e.g. the face class). Assuming a normal distribution, the

estimate for the Mahalanobis distance is also a weighted sum of the DFFS and the DIFS

similar to equation . A main difference isthat our scaling factor c is much smaller than the

one used in because the DIFS provides more information when using the skin probability

image instead of the luminance image. The distance map using the combined error

criterion is shown in fig. 5.1.The global minimum (marked by the white circle) lies

in the true face region and the face is detected correctly. Fig. 5.1 shows the detected face

region super imposed on the luminance component.To detect faces at multiple scales, the

detectionalgorithm is performed on several scaled versions ofthe skin probability image

and the global minimum of the resulting multi-scale distance maps is selected.


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5.2 Detection of more than one face

Detection results for severalimages of MPEG test sequences are shown in fig

5.2.The global minimum detection based on the assumption that the image contains

exactly one face. To detect several faces and reject non-facial images, athreshold can be

introduced which allows a tradeoff between false detection and false rejection. Ifthe error

criterion at a certain spatial position is less than this predetermined threshold, the

subimage located at this position is classified as a face. To prevent overlapping regions,

only the global minimumis selected in the next step. For the search of the second (local)

minimum, all spatial positions which leadto overlapping regions are discarded. This

procedure is repeated until no position with error less thanthe threshold can be found

which is not already covered by another detected region. The result of a multiple face

detection is given in fig 5.2.

5.4 APPLICATIONS

1. Biometrics

2. A Facial Recognition System

3. Video Surveillance

4. Human Computer Interface

5. Image Database Management

6. Webcam etc..

Face detection is used in biometrics , often as a part of (or together with) a facial

recognition system . It is also used in video surveillance, human computer interface and

image database management. Some recent digital cameras use face detection for

autofocus .[1] Face detection is also useful for selecting regions of interest in photo

slideshows that use a pan-and-scale Ken Burns effect .

Face detection is gaining the interest of marketers. A webcam can be integrated into a

television and detect any face that walks by. The system then calculates the race, gender,

and age range of the face. Once the information is collected, a series of advertisements

can be played that is specific toward the detected race/gender/age.

With increasing research in the area of face segmentation, new methods for detecting human faces automatically are being developed. However, less attention is
http://en.wikipedia.org/wiki/Biometricshttp://en.wikipedia.org/wiki/Biometricshttp://en.wikipedia.org/wiki/Biometricshttp://en.wikipedia.org/wiki/Facial_recognition_systemhttp://en.wikipedia.org/wiki/Facial_recognition_systemhttp://en.wikipedia.org/wiki/Facial_recognition_systemhttp://en.wikipedia.org/wiki/Facial_recognition_systemhttp://en.wikipedia.org/wiki/Face_detection#cite_note-1http://en.wikipedia.org/wiki/Face_detection#cite_note-1http://en.wikipedia.org/wiki/Face_detection#cite_note-1http://en.wikipedia.org/wiki/Ken_Burns_effecthttp://en.wikipedia.org/wiki/Ken_Burns_effecthttp://en.wikipedia.org/wiki/Ken_Burns_effecthttp://en.wikipedia.org/wiki/Ken_Burns_effecthttp://en.wikipedia.org/wiki/Face_detection#cite_note-1http://en.wikipedia.org/wiki/Facial_recognition_systemhttp://en.wikipedia.org/wiki/Facial_recognition_systemhttp://en.wikipedia.org/wiki/Biometrics


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being paid to the development of a standard face image database to evaluate these new

algorithms. This paper recognizes the need for a colour face image database and creates

such a database

for direct benchmarking of automatic face detection algorithms. The database has two

parts.Part one contains colour pictures of faces having a high degree of variability in

scale, location, orientation, pose, facial expression and lighting conditions, while part

two has manually segmented results for each of the images in part one of the database.

This allows direct comparison of algorithms. These images are acquired from a wide

variety of sources such as digital cameras, pictures scanned in from a photo-scanner and

the World Wide Web. The database is intended for distribution to researchers. Details of

the face database such as the development process and file information along with a

common criterion for performance evaluation measures is also discussed in this paper.

Key Words: Face Detection, Colour Face Image Database, Evaluation Performance.

CHAPTER 6

RESULTS AND ANALYSIS

6.1 Assessment of Success Rate

Here we have assessed the dependency of output on the number of training images

(per individual).

Total Number of

Images

Number of Training

Images( per

individual)

Number of Test

Images(per

individual)

Success Rate (%)


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50 1 5 72.3%

50 5 5 87%

50 10 1 88%

6.2 Advantages

It is simple and fast, and it only needs a small amount of memory. PCA basically performs dimensionality reduction.

Smaller representation of database because we only store the training images in

the form of their projections on the reduced basis.

Noise is reduced because we choose the maximum variation basis and features

like backgrounds with small variations are automatically ignored.

It is quite efficient and accurate in terms of success rate.

6.3 Limitations

Mainly frontal face is required .

Multiscaling is a problem .

Background variations and lightning conditions still pose a difficulty .

6.4 Application Areas

Access Control

1. ATM

2. Airport

3. Lockers

Entertainment

1. Video Game

2. Virtual Reality

3. Training Programs


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4. Human-Computer-Interaction

5. Human-Robotics

6. Family Photo Album

Smart Cards

1. Drivers Licenses

2. Passports

3. Voter Registrations

4. Welfare Fraud

5. Voter Registration

Information Security

1. TV Parental control

2. Desktop Logon

3. Personal Device Logon

4. Database Security

5. Medical Access

6.

Internet Access

Law Enforcement and Surveillance

1. Advanced Video surveillance

2. Drug Trafficking

3. Portal Control

Multimedia Management

1. Multimedia management is used in the face based database search

Some Commercial Applications

1. Motion Capture for movie special effects.

2. Face Recognition biometric systems.


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3. Home Robotics.

CONCLUSION

We are currently extending the system to deal with a range of aspects (other than

full frontal views) by defining a small number of classes for each known person

corresponding to characteristic views. Because of the speed of the recognition, the system

has many chances within a few seconds to respond to attempt to recognize many slightly

different views.

In this project we store a set of images in the database ,whenever we input a image that

we want to test and if it is in the database will be recognized using the Eigen face

algorithm and the reconstructed face will the output image. Here we are not using any

filters but we are simply recognizing by reconstructed images of the input. And parallel

the Euclidean distances also will be measured.


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The eigenface approach to face recognition was motivated by information

theory, leading to the idea of basing face recognition on a small set of image features that

best approximate the set of known face images, without requiring that they correspond to

our intuitive notions of facial parts and features. Although it is not an elegant solution to

the general object recognition problem, the eigenface approach does provide a practicalsolution that is well fitted to the problem of face recognition. It is fast, relatively simple,

and has been shown to work well in a somewhat constrained environment.

FUTURE SCOPE

This project is based on eigenface approach that gives the accuracy maximum

92.5%. There is scope for future using Neural Network and Active Appearance Model

techniques that can give better results compared to eigenface approach. With the help of neural network technique accuracy can be improved.

The whole software is dependent on the database and the database is dependent on

resolution of camera, so in future if good resolution digital camera or good resolution

analog camera will be in use then result will be better used. Real time video processing .

As for now, the threshold value is difficult to set in real time.Using OpenCV, an external

platform provided by Intel consisting of inbuilt libraries and functions, face recognition

can be made easier.

REFERENCES

[1] Zhujie, Y. L. Yu. Face Recognition with eigenfaces, Proceedings of the IEEE

International Conference on Industrial Technology, pp. 434-438., 1994.

[2] Lizama, E.; Waldoestl, D.; Nickolay, B., An eigenfaces -based automatic face

recognition system

IEEE International Conference Systems, Man, andCybernetics, 1997. Volume 1, Issue 12-15 Oct 1997 Page(s):174 177.

[3] M.Turk and A.Pentland; Face Recognition Using Eigenfaces, Proceedings;

IEEE Conference on Computer Vision and Pattern Recognition, pages 586-591,

1991.

[4] Pablo Navarrete Javier Ruiz-del- Solar Analysis and Comparison of Eigenspace

Based Face Recognition Approaches IJPRAI 16(7): 817-830 (2002).


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[5] H.K. Ekenel, J. Stallkamp, H. Gao, M. Fischer, R. Sti efelhagen.; Face

Recognition for Smart Interactions, IEEE International Conference on

Multimedia & Expo, Beijing, China, July 2007. Page(s):1007 1010.

[6] Alex Pentland, BabackMoghaddam, and Thad Starner, View -Based and Modular

Eigenspaces for Face Re cognition, IEEE Conf. on Computer Vision and Pattern Recognition , MIT Media Laboratory Tech. Report No. 245 1994.

[7] http://www.face-rec.org/[8] http://stackoverflow.com/[9] http://www.mathworks.in/[10] http://cnx.org/content/m12531/latest/[11] http://www.pages.drexel.edu/~sis26/Eigenface%20Tutorial.htm#Programming[12] http://www.facerecognition.it/[13] http://www.shervinemami.info/faceRecognition.html

6.report face recognition

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