ADVANCES in NATURAL and APPLIED SCIENCES

ISSN: 1995-0772 Published BYAENSI Publication EISSN: 1998-1090 http://www.aensiweb.com/ANAS

2017 May 11(7): pages 139-144 Open Access Journal

ToCite ThisArticle: P. Arun Mozhi Devan, M. Venkateshan, A. Vignesh, S.R.M. Karthikraj., Smart Attendance System Using Face Recognition. Advances in Natural and Applied Sciences. 11(7); Pages: 139-144

Smart Attendance System Using Face Recognition

1P. Arun Mozhi Devan, 2M. Venkateshan, 3A. Vignesh, 4S.R.M. Karthikraj

1Assistant Professor, 2,3,4Student Scholars Electronics and Instrumentation, Sri Ramakrishna Engineering College, Coimbatore. India. Received 28 Feb 2017; Accepted 14 May 2017; Available online 19 May 2017

Address For Correspondence: P. Arun Mozhi Devan, Assistant Professor, Student Scholars Electronics and Instrumentation, Sri Ramakrishna Engineering College, Coimbatore. India. E-mail: arunmozhideven.p@srec.ac.in

Copyright © 2017 by authors and American-Eurasian Network for ScientificInformation (AENSI Publication). This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/

ABSTRACT Most representative part of body can be used to differentiate one person among others is face and it is a fundamental technology of biometrics, which has been applied to a variety of areas like computer vision, security systems (pattern recognition), human machine interaction and image processing. In this proposed approach the features of the query image (current facial image) and database images (stored facial image) features have been extracted by Viola Jones algorithm and trained using ANN. This paper is to increment the output neurons simultaneously with incrementing the input patterns started with a small number of output neurons and a single hidden-layer using an initial number of neurons. Face recognition experiments were carried out by using Artificial Neural Networks. The main objective of this proposed system is to develop an efficient face recognition system by improving the efficiency of the existing face recognition systems and also for the process of secured attendance system. This attendance system uses the mapping of the images with the database and it updates the days of present and also notifies the external person’s presence.

KEYWORDS: Viola Jones algorithm, vital feature points,AdaBoost classifier, face identification, facial features, mapping,

Artificial Neural Networks (ANN)

INTRODUCTION

A facial recognition system is a computer application capable of identifying a person from a digital image

or a video frame from a video source. One of the ways to do this is by comparing selectedfacial features from

the image and a facial database. An Attendance Management System developed using biometrics generally

consists of Image Acquisition, Database development, Face detection, Pre-processing, Featureextraction and

Classificationstages followed by Postprocessingstage. Traditionally attendances are taken manually by using

attendance note available for class, office, etc. which is a time-consuming event. Moreover, it is very difficult to

verify one by one in a large environment withdistributed branches whether the authenticated persons are actually

responding or not. Other biometrics likefingerprints, iris scans, and speech recognition cannot performthiskind

of mass identification.Inthispaper, proposed system takes the attendance of peopleduringworking hourswhich

has been implemented in MATLAB software.

This system takes theattendance automatically using face recognition. However, it is difficult to estimate

the attendance precisely usingeach result of face recognition independently because the face detection rate is not

sufficiently high.The proposed methodestimates the attendance precisely using all the results of face recognition

obtained bycontinuous observation and it improves the performance for the estimation of the attendance.

Thispaper first review the related works in the field of attendance management and face recognition. Finally,

experiments are implemented to provide as evidence to support our plan. Theresult shows that continuous

observation improved the performance for the estimation of the attendance.

140 P. Arun Mozhi Devan et al., 2017/Advances in Natural and Applied Sciences. 11(7) May 2017, Pages: 139-144

II. Related Work:

In [1] the authors have proposed a finger print based attendance system. A portable fingerprint device has

been developed which can be passed among the students to place their finger on the sensor during the lecture

time without the instructor’s intervention. This system guarantees a fool-proof method for marking the

attendance. The problem with this approach is that passing of the device during the lecture time may distract the

attention of the students.

In [2] the authors have proposed RFID based system in which students carry a RFID tag type ID card and

they need to place that on the card reader to record their attendance. This system may give rise to the problem of

fraudulent access. An unauthorized personmaymake use of authorizedID card and enter into the organization.

In [3] the authors have proposed Dousman’s algorithm based Iris recognition system. This system uses iris

recognition management system that does capturing the image of iris recognition, extraction, storing and

matching. But the difficulty occurs to lay the transmission lines in the places where the topography is bad.

III. Proposed Framework:

The proposed automated attendance management system is based on face recognition algorithm. When a

person enters the room his image is captured by the camera at the entrance. Face region is then extracted and

pre-processed for further processing. Face Recognition proves to be advantageous than other systems are

discussed in the Table I. The stages in the proposed Automated Attendance Management System are as shown

in the Fig.1 Technical details of implementation of each stage are discussed in the next sections.

Fig. 1: System block diagram

Table I: Comparison Of Various Attendance Systems Type of the System Drawback

RFID-based Fraudulent usage

Finger print based Time Consuming for people to wait and give their attendance

Iris-based Invades the privacy of the user

Wireless-based Poor performance if topography is bad

A. Feature Points on Human Face:

Applying human visual property in the recognition offaces, people can identify face from very far distance,

even the details are vague. Human faces made up of eyes, nose, mouth and chin etc. Thereare differences in

shape, size and structure of thoseorgans, so the faces are differ in thousands ways. One commonmethod is to

extract the shape of the eyes, nose, mouth and chin, and then distinguish the faces bydistance and scale of those

organs.Wecan normalize the faces in the database through pre-treatment, so as to extend the range of database,

reduce the storage and recognize the faces moreeffectively.

141 P. Arun Mozhi Devan et al., 2017/Advances in Natural and Applied Sciences. 11(7) May 2017, Pages: 139-144

Additionally, the selection of face feature point’s iscrucial to the face recognition. The number of the

feature points should take enough information and not be too many. If the database has different post rues of

each people to be recognized the property of angle in variance of the geometry characteristicis

veryimportant.This paper haspresented a method to locate the vital feature points offace, which select 9 feature

points [4] that have theproperty of angle invariance, including 2 eyeballs, 4near and far corners of eyes, the

midpoint of nostrilsand 2 mouth corners, as shown in Fig.2

Fig. 2: The 9 vital feature points on face with extracted value of image

Where,

C1 = Left eye center-Eye center

C2 = Right eye center-Eye center

C3 = Left eye center-Mouth center

C4 = Right eye center-Mouth center

C5 = Left eye center-Nose center

C6 = Left eye center-Nose center

B. Face Detection:

A proper and efficient face detection algorithm alwaysenhances the performance of face recognition

systems.Various algorithms are proposed for face detection such asFace geometry based methods, Feature

Invariant methods,Machine learning based methods. Out of all these methodsViola Jones [5] proposed a

framework which gives a highdetection rate and is also fast.Viola-Jones detection algorithm [10] is efficient for

real timeapplication as it is fast and robust. Hence we choseViola-Jones face detection algorithm which makes

useof Integral Image and AdaBoost learning algorithm asclassifier [6]. We observed that this algorithm gives

betterresults in different lighting conditions and we combinedmultiple Haar classifiers to achieve a better

detection ratesup to an angle of 55 degrees.

C. Database Development:

As we chose biometric based system enrolment of everyindividual is required. This database development

phaseconsists of image capture of every individual and extractingthe bio-metric feature, in our case it is face

features and laterit is enhanced using pre-processing techniques and1 storedin the database. In our project we

have taken a database of13individuals with6 images of eachhas been collected for this project.

D. Feature Extraction and Classification:

The performance of a Face Recognition system also depends upon the feature extraction and their

classification to get the accurate results. Feature extraction is achieved using feature based techniques or holistic

techniques. In some holistic techniques we can make use of dimensionality reduction before

classification.Principal Component Analysis (PCA) was the first algorithm that represents the faces

economically. In PCA the face images are represented using Eigen faces and their corresponding projections

along each Eigen face. Insteadof using allthe entire dimensions of an image only meaningful dimensions are

considered to represent the image. Mathematically an image using PCA is represented as

X= WY + μ

WhereXis the face vector, Y is vector of Eigen faces, W isthe feature vector, and μ is the average face

vector. In general features extracted from PCA and Linear Discriminant Analysis (LDA)is subjected to distance

classifiers [7]. The distance between the features of probe image and features of trained images is

calculated.PCA is used for feature extraction and Support VectorMachine (SVM) is used for the

142 P. Arun Mozhi Devan et al., 2017/Advances in Natural and Applied Sciences. 11(7) May 2017, Pages: 139-144

classification.For recognition SVM findsthe optimal separation of closest points in the training set and we

require a multi-class classification. SVMSupport Vector Classification atype is used formulti-class

classification. In classification of imagessmall amount of trainingdata [8] is enough for estimation. So Face

Recognition involves in two stages, featureextraction and classification. The above mentioned featureextractors

combined with classifiers are compared invarious real world scenarios such as lighting conditions,Unintentional

facial feature changes and Expressions.

E. System Accuracy:

System Performance is also evaluated in termsof recognition rate, distance, false positive rate, timetaken for

training. This system can be employed for ‘n’ number of peoples, but for our demonstration we have taken our

class students as an example and False Positive Rates are calculated byconsidering 13 real time image frames

and system accuracy will be given as system accuracy and it will be denoted as

𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =TP+TN

TP+TN+FP+FN (1)

TP defined that face is correctly identified (out of 40 correct faces for the same person); TN implies that

faces is correctly rejected (out of remaining 25 wrong persons). FP imp lies that face is incorrectly identified,

and FN equals face is incorrectly rejected. Our aim is to maximize TP and TN and minimize FP and FN. This

can be explained with the MSE of NN and the number of epochs taken to converge with the desired goal of our

trained value and it is shown in Fig.3.

Fig. 3: Performance and accuracy of trainedset of images

Training state results of the neural network (in epochs) has been shown in Fig.4.Distance also playsas a

criterion in this system model as the face image frames arecaptured from the video and face region isresized. So

the face region captured at about 4feet to17feet gives better results.For a Training data of 13 images trainingtime

is calculated.Algorithm requires minimumtime for training where as SVM and Bayes classifiers takemore time

for training. In classifiers comparison SVM doesbetter classification than the rest.Dewi’s [9] system accuracy is

around 93.7 % while the proposed system accuracy is 98.14 %. Regarding computation time, their system spent

3.52 seconds while the proposed system takes 1.56 seconds.

Fig. 4: Training state results of the neural network (in epochs)

143 P. Arun Mozhi Devan et al., 2017/Advances in Natural and Applied Sciences. 11(7) May 2017, Pages: 139-144

IV.Results:

In the proposed system, after recognizing the faces ofthe students (shown in Fig.5) the students roll

numbers are updated into an excel sheet shown in Fig.6.At the end of the class a provision to announce

thenames of all students who are present in the class isalso included.

Fig. 5: Recognized faces of the students

The system is also equipped with the facilityof sending notification messages to the absentees when

thatfacility is enabled.For detecting face and its features using the Viola Jones cascaded object detection

framework, it takes 0.8168 seconds toprocess single 640 x 480 pixels image.This system also notifies the

unwanted person’s like lecturers, other class students and workers (Untrained Faces)interference with in the

class which is shown in Fig.7

Fig. 6: Updated attendance into an excel sheet

Fig. 7: Untrained face popup notification

144 P. Arun Mozhi Devan et al., 2017/Advances in Natural and Applied Sciences. 11(7) May 2017, Pages: 139-144

V.Conclusion And Future Scope:

Current work is focused on the method to obtain the different weights of each focused face based on

location. The proposed methods have provided rather good facial feature detection. Examples demonstrating the

success of the various proposed methods as well as criticism when they fail have been included. We proposed a

complete framework for accurate human face-based identification system. Face not only detected using this

algorithm but also the distance of the facial characters. Also it is detected in both colour and grayscale images

under varying conditions.

Success rate at face recognition is around 93% to 95% and regarding face identification is 99%. The system

error rate was around 5-7%. Using 6 face distances was good enough to distinguish faces, and using more

distances can make result better. This face recognition also used for the process of making the attendance of the

students.In future multiple face recognition can be improved by integratingvideo-streaming service and lecture

archivingsystem, to provide more profound applications in thefield ofdistance education, course management

system(CMS) and support for faculty development (FD).

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