real time facial recognition

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International Journal of Electrical, Electronics and Data Communication, ISSN: 2320-2084, Volume- 1, Issue- 4

Real Time Face Recognition System For Time and Attendance Applications

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REAL TIME FACE RECOGNITION SYSTEM FOR TIME AND

ATTENDANCE APPLICATIONS 1APARNA BEHARA, 2M.V. RAGHUNADH

Department of E and CE, NIT Warangal, INDIA 506004Email: [email protected], [email protected]

Abstract — In this paper, we have proposed an automated Face Recognition System for Time and Attendance application.The model is developed with the help of real time OpenCV library The proposed system comprised of using the Viola Jonesalgorithm for detecting the human faces and then the detected face is resized to the required size, this resized face is further processed by using linear stretch contrast enhancement and finally it is recognized using a simple PCA / LDA. Once

recognition is done, automatically attendance will be updated in an Excel Sheet along with his name, date and time. An htmlfile is automatically updated by our system so that a remote authenticated user can access the attendance file .Our system is

integrated to an Automatic Attendance Management System, with the help of which some post attendance works like stipendamount calculation, viewing attendance report for the required date, calculating the number of hours a person is present inthe class, searching for a required person in the classroom etc. Spoofing which is a major threat for our system can be

avoided using Eye Blink Detector algorithm. Our system can automatically update the Database for the newly enrolled persons. The proposed system can execute the instructions given orally to it (eliminating the need of a mouse interface) and

can speak back to us.

Keywords — Face, Attendance, Eye blink, GUI

I.

INTRODUCTION

Person Recognition is one of the emergingresearch fields in computer vision. The majorapplication for person recognition is for ClassroomAttendance system, where the attendance is

automatically updated onto an attendance sheet andalso the IN/OUT timings of the Students can be

stored as a soft data for later records. This system isdeveloped in order to avoid the manual drudgery forlecturers in entering the data daily while takingattendance and also to avoid proxy.

They are several biometrics used for Person

Recognition like Iris, Fingerprints, Face etc. SinceIris and Fingerprints are very short-distance biometrics, but our application requires a person to beat a medium distance from the camera, which is fixedat the centre of the classroom near the black board, so

that the view of the camera covers the entireclassroom. As a worst case, our system should beable to recognise a person who is sitting at the last

bench, which might not be possible by using Iris orFingerprints as a biometric. Hence we go for amedium range Biometric i.e., Face with the help ofwhich we can recognise a person and mark his

attendance.

The rest of the paper is organized as follows: Thedetailed literature survey is given in section II, the proposed model is explained in section III, the

experimental results are shown in section IV, andfinally conclusion and future scope are discussed insection V.

II.

LITERATURE SURVEY

The research going on for Face Detection and

feature extraction are as discussed below:

A. Face Detection Methods:The different techniques used for face detection

are as classified as shown below:

Knowledge Based Method

Feature Invariant Method

Template Matching Method

Appearance Based Method

B. Face Feature Extraction Methods:The techniques used for Face Recognition can

be divided into two main categories:

Holistic Approach

Feature-based Approach

In the holistic approach, the whole face is taken asinput for recognition purpose. We can use PCA

(Principal Component Analysis), for reducing the

dimensionality of the data by projecting, it onto alower dimensional subspace [1]. We can go for LDA

(Linear Discriminate Analysis), where thedimensionality reduction takes place such that thewithin class variance is reduced and between class

variance is maximised [2].In Feature –based Approach, local features on face

such as eyes and nose are detected and based upon

which recognition is performed [3].

III.

PROPOSED MODEL

The proposed model for Face Recognition system is

as shown in Fig.1.





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Fig. 1. Face Recognition Model





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A. Face Recognition SystemThe proposed model for face recognition system is

as shown in Fig. 1.

The main modules used are

1) Pre-processing:

This stage consists of 3 main steps:

a) Face Detection: Viola–Jones face detectionalgorithm is used for Face detection.

b) Resizing: Once face is detected, it is resizedto a fixed resolution of 92 *112 pixel resolution

c) Contrast Enhancement: For betterrecognition accuracy, we have used Min-Max LinearContrast Stretch Enhancement Technique,

2) Feature Extraction and Recognition

For feature extraction and recognition we have

used PCA/LDA Algorithm. Since we want real time processing of data, we have chosen the simplePCA/LDA algorithms where computationalcomplexity is less when compared to the other facerecognition algorithms.

Fig. 2. Eye Blink Detector

3) Eye Blink Detector: Spoofing attack (copyattack) is a major threat for our proposed system. Ifinstead of a live person, if we keep a photograph, itmarks the attendance of the person onto the Excelsheet, which shouldn’t be the case for

Classroom/Employee attendance Applications.Figure. 2 show the eye blink detector algorithm.

IV.

EXPERIMENTAL RESULTS

A. Database Used1) NIT Database: It is the database collected

from the 40 students of NIT Warangal. NIT face

database consists of frontal facial images taken underdifferent days, different lighting conditions, differentmoods (smiling/crying), with and without spectacles

etc.

B. Graphical User Interface (GUI):

Figure 9,10 shows the GUI that were made usingMicrosoft Visual Studio 2010, as a Windows Forms

Application using Visual C++ as a programminglanguage and making use of OpenCV 2.4.2 (computer vision library written in c++) and together

with some add on visual effects in the GUI .

As shown in the GUI as shown in Fig 9, certain provisions are provided for selecting few options like

• Database Selection: ECE / CSE Database• Algorithm Selection: PCA /LDA Algorithm• Image Type Selection: Offline/Real-Time

• Camera Selection :Blackboard /Frontal Door• Specialization :ACS / VLSI/ EIE

Identified person announcement button which has the

Microsoft text to speech converter integrated in it toannounce the name of the recognized person as“Mr.xxx. is present”. Update Attendance button will

automatically update the attendance along with timeand date of his arrival in an Excel sheet as shown in

Fig 3.Update html button will allow access for aremote authenticated user to retrieve the attendancesheet as shown in Fig 4 and Fig.5. Figure 6 shows theLogin form for Principal /Head of the Department.

Once authenticated GUI as shown in Fig.10 getsopened, where we have certain options like searchinga particular person in a classroom with the help of his

image, an attendance sheet as shown in Fig. 7.Chartsdepicting the number of hours an Employee worksand attendance percentage for the chosen month as

shown in Fig. 8 and Fig.11.And also we have options for applying leave andstipend calculation.

Fig. 3. Excel sheet with Attendance marked





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Fig. 4. Html Login Page

Fig. 5. Html page –Attendance Report

Fig. 6. GUI Attendance Management System :Login Page

Fig. 7. GUI:Attendance Report

Fig. 8. GUI:Graph plotting number of hours worked on each

day





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Fig. 9. GUI developed : Face Recognition System

Fig. 10. GUI developed : Attendance Management System





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Fig. 11. GUI:Graph plotting attendance percentage for each

student

C. Analysis

Table I shows the comparison of the recognition rates between offline images and for Webcam videos usingPCA for varying Database size.

Table IPCA Algorithm

Database Size Offline Images

( Recognition

Real time Video

(Recognition2 0.6 0.56

3 0.7 0.64

4 0.79 0.70

5 0.8 0.75

6 0.85 0.79

7 0.89 0.86

8 0.9 0.88

9 0.93 0.91

Similarly Table II shows the comparison using LDA

Algorithm.

Table IILDA Algorithm

Database Size Offline Images

( Recognition

Real time Video

(Recognition

2 0.62 0.60

3 0.8 0.72

4 0.82 0.75

5 0.85 0.82

6 0.9 0.87

7 0.89 0.85

8 0.8 0.79

9 0.82 0.76

CONCLUSION AND FUTURE SCOPE

Due to the mounting demands for an AutomatedAttendance System, Person Recognition, systemshave gained a lot of importance these days. Hence

depending upon the size of the Training Database,recognition rate varies for both of thesealgorithms.And also we have observed that therecognition rate achieved for real time is mouch

lesser when compared to offline images.Performance of the proposed system reduces by 3-5%of recognition accuracy, when tested with or withoutspectacles. Typical variations where our system islikely to fail are intentional masking of the face

/tonsuring of the head /beard/obscuring the face with

a scarf. Other type of Biometrics like Gait can beused for better recognition in longer distance

recognition.

REFERENCES

[1] M. Turk, A. Pentland, “ Eigenfaces for Recognition”, Journal of

Cognitive Neurosicence, Vol. 3, No. 1, 1991, pp. 71-86

[2] J. Lu, K.N. Plataniotis, A.N. Venetsanopoulos, “Face

Recognition Using LDA-Based Algorithms” , IEEE Trans. on

Neural Networks, Vol. 14, No. 1, January 2003, pp. 195-200

[3] Ramesha, K and Raja “Feature extraction based face

recognition, gender and age classification” ,(IJCSE)

International Journal on Computer Science and Engineering,

Vol. 02, No.01S, 2010,pp 14-23.

real time facial recognition

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