attendance monitoring using real time face...

ISSN: 2278 – 909X International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE)

Volume 5, Issue 5, May 2016

1308

All Rights Reserved © 2016 IJARECE

Abstract— Employee or student attendance

monitoring is simplified by face recognition

technology by using Matlab and sending SMS to a

person who is not present by using GSM technology.

Since Matlab is a high level language it can be easily

understood by the beginners. This system is very

efficient and requires very less maintenance compared

to the traditional methods. There are many methods

for face recognition like LDA, PCA, Neural networks,

ICA. Among all these methods PCA is the most

efficient technique. In our project we implement PCA

algorithm for face recognition and based on

recognized faces attendance is monitored for database

and SMS is sent to absentees using GSM .

Index Terms—Attendance, PCA (Principal Component

analysis), Eigen faces, GSM.

I. Introduction

Preserving the attendance is very crucial in all the

institutes for checking the overall performance of students.

Each institute has its very own method in this regard. A few

are taking attendance manually using the old paper or

document based approach and some have adopted techniques

of automated attendance the use of few biometric techniques.

There are many computerised methods to be had for this

reason i.e. biometric attendance. All these methods

additionally waste time due to the fact that college students or

employees have to make a queue to contact their thumb on

the scanning device. This gadget makes use of the face

recognition approach for the computerised attendance of

students in the study room environment without lectures

intervention or the employee .This attendance is recorded

with the aid of usage of a digital camera connected in the

study room or the working environment i.e. constantly

shooting photos of students or employees, discover the faces

in pix and examine the detected faces with the database and

mark the attendance.

II. Related Work

The antique technique for taking attendance is manual work.

However this approach takes a lot of time and there are

possibilities that the attendance is now not marked well. The

2D method is finger print repetition. However for some

people it is intrusive, due to the fact its miles nonetheless

related to criminal identity. Any other drawback of finger

print recognition is that it can make errors with the dryness

or dirt of the fingers, pores and skin. The technique for taking

attendance is Iris recognition. The downside of this

technique is it is also intrusive and plenty of memory is

required for records garage.

There are various techniques for facial recognition like Eigen

face method. Diverse extensions have been made to Eigen

face method such as eigenfeatures.This technique combines

facial metrics (measuring distance among facial capabilities)

with the Eigen face illustration. Any other technique

comparable to the Eigen face technique is „Fisher Faces‟

which uses linear discriminant evolution. This approach for

facial recognition is much less sensitive to variant in lights

and pose of the face than usage of Eigen faces. Fisher faces

utilize labeled data to preserve greater of the magnificence

particular information throughout the measurement discount

Stage further opportunity to Eigen faces an fisher faces is the

energetic look version. This approach use an energetic shape

model to describe the outline of a face by collecting many

outlines, primary issue analysis can be used to form a

foundation set of fashions which encapsulate the variation

of exclusive faces. Many current procedures use principal

factor analysis as a means of measurement discount or to

form foundation images for extraordinary modes of version.

The method incorporated in this project is PCA using Eigen

face approach.

III. Existing model for face

recognition

Face recognition biometrics is the science of programming a

computer to recognize a human face. When a person is

enrolled in a face recognition system, a video camera takes a

series of snapshots of the face and then represents it by a

unique holistic code.

ATTENDANCE MONITORING USING

REAL TIME FACE RECOGNITION IN

MATLAB

Ramya.C N, Anusha.B E, Lakshmi.V, Lalitha.S, Abhilasha.A S



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When someone has their face verified by the computer, it

captures their current appearance and compares it with the

facial codes already stored in the system. The faces match,

the person receives authorization; otherwise, the person will

not be identified. The existing face recognition system

identifies only static face images that almost exactly match

with one of the images stored in the database.

IV. Proposed model

Fig1. Block diagram of the face recognition system

The device is composed of a digital camera that captures the

pictures of the students or employees and sends it to the photo

enrollment module. In enrollment module, snapshots are

greater so that matching can be carried out without difficulty.

After enrollment, the image comes in the face detection and

reputation modules and then the attendance is marked in the

database. At the time of enrollment, templates of face

photograph of manual man or woman students are saved in

the database. Right here all the faces are detected from the

input image and the set of rules compares them one by using

one with face database. If any face is recognized the

attendance is marked in the data base from in which all of us

can get entry and use it for specific purposes.

Teachers come in the class and just press a button to

begin the attendance manner and the system robotically gets

the attendance without even the intentions of college students

and teacher. In this manner a lot of time is saved and this is

exceedingly securing process nobody can mark the

attendance of different continuously to it upon and

apprehends all the students in the school room.

The gadget functions with the aid of projecting face image on

to the feature space that spans the large variations among

recognized face photographs .The large capabilities are

regarded as “Eigen faces” ,because they are the Eigen

vectors(essential elements) of the set of faces they do not

necessarily correspond to the function such as eyes ,ears and

noses .The projection operation characterize and character

face by the weighted sum of the Eigen faces functions and so

to understand a unique face it is necessary simplest to

evaluate those weights to the ones people.

V. Implementation

Fig2. Enrollment process

Yes no

recognized not matching

Fig.3 Detailed steps in PCA

First step in PCA is to convert each of the face in training set

into the vector form called face vector and next step to

normalize these face vectors which means to remove the

common features present in all images of training set so that

each face image will be left out with its unique feature

To do that we need to calculate the average face vector of all

training set images and subtract it from selected image and if

they have a minimum value matching with that of original

image than it is recognized or else not matching .

It mainly consists of three steps:

1. Feature extraction.

2. Face location detection.

3. Facial image classification.

The main features extracted are Eigen faces which are

mathematically computed and face location detection

performs the function of identifying the image which are

present in the database image and in the final stage the image

is classified whether or not it is recognized. The

Compute weight vector

of input image

Calculate distance between

input weight vector and all

training set

Min_

Distan

ce?

Convert to

face vector

Normalize

face vector

Project into

Eigen space

Capture

image

Feature

extraction

database



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mathematical steps for feature extraction are as shown

below:

STEP 1: Prepare the Data

The first step is to obtain a set S with P face images.

Each image is transformed into a vector of size N and placed

into the set.

S={T1,T2……………TP}

STEP 2: Obtain the Mean

After obtaining the set, the mean image M has to be

obtained as,

STEP 3: Subtract the Mean from Original Image

The difference between the input image and the mean

image has to be calculated and the result is stored in A.

Ai=Ti-M STEP 4: Calculate the Covariance Matrix

The covariance matrix C is calculated in the following

manner

STEP 5: Calculate the Eigenvectors and Eigenvalues of

the Covariance Matrix and Select the Principal

Components

In this step, the eigenvectors (Eigen faces) V and the

corresponding eigenvalues D should be calculated. The

higher the eigenvalue, the more characteristic features of a

face does the particular eigenvector describe. Eigenfaces

with low eigenvalues can be Omitted, as they explain only a

small part of the characteristic features of the faces. After

Eigen faces are determined, the “training” phase of the

algorithm is finished. Once the training set has been prepared the next phase is the classification of new input

faces. We implement this face recognition process in matlab using

GUI functions. In GUI there is a callback function which is

used to callback the each function used in the code such as

capture video ,crop image ,save ,exit and recognize .In our

project we use Microsoft excel sheet for databse storage of

student information like phone number ,their Roll number

and array is maintained for attendance marking and if

student is not recognized during testing than SMS is sent to

that particular student by fetching the data from the database

which we had stored .

VI Result

1. Main functions used in GUI for face recognition and

their callback to the code

Fig 3. callback view of GUI functions used

2. Face detection and image capturing

Fig4. Face detection



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3. Saved images in the database

Fig5. Database images

4. Real time recognition of face with that of the images

stored in the database.

Fig6. face recognition

5. Student information stored in Microsoft excel

Fig7. student database

6. Using GSM Technology sending SMS to the

absentees

Fig8.GSM 900

7. snapshot of the SMS sent to the absentee.

Fig9.sms sent to absentee

VII Conclusion

It can be concluded from the above dialogue that a

dependable, secure ,rapid and an efficient system has been

evolved changing a guide and an unreliable system .This

process can be carried out for higher outcomes regarding the

control of attendance .this system will keep time ,reduce the

quantity of work the administration has to do and will update

stationary material with digital apparatus .Every other

application of this machine is that it is capable of marking the

presence of personnel at any place of work and this

attendance will be useful for calculating their month to

month payment .



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VIII References

[1] http://www.face-rec.org

[2] Yong Zhang, Member, IEEE, Christine

McCullough, John R. Sullins, Member, IEEE

Hand-Drawn Face Sketch Recognition by Humans

and a PCA-Based Algorithm “IEEE

TRANSACTIONS ON SYSTEMS, MAN, AND

CYBERNETICS SYSTEMS AND HUMANS”,

VOL. 40, NO. 3, MAY 2010.

[3] K. W. Bowyer, K. Chang, P. J. Flynn, and X. Chen,

“Face recognition using 2-D, 3-D and infrared: Is

multimodal better than multisampling?”Proc.

IEEE, vol. 94, no. 11, pp. Nov.2012.

[4] G. Medioni, J. Choi, C.-H. Kuo, and D. Fidaleo,

“Identifying no cooperative subjects at a distance

using face images and inferred three-dimensional

face models,” IEEE Trans. Syst., Man, Cybern . A,

Syst., Humans, vol. 39, no. 1, pp. 12–24, Jan. 2009.

About authors

Prof. Ramya C N

Ramya C N has completed B.E in telecommunication

engineering and M-Tech in digital electronics and

communication systems (DECS).Currently working as a

professor in electronics and communication engineering at

Atria Institute of Technology, affiliated to Visvesvaraya

Technological University, Bangalore, Karnataka, India.

Has published a “Multimodal Biometrics System using

Phase-based matching technique” in NCETCS-2010-Paper.

ID-2.038

Attended workshop on “Signal and Image Processing”

organized by Department of E&C With cranes software

international ltd DRDO New Delhi.

Lakshmi V

Lakshmi was born on 28th august 1994, and completed her

secondary education from Florence public high school and

pre university course from St. Anne‟s PU college for girls and

currently pursuing her bachelor degree in engineering in

Atria Institute of Technology, Bangalore.

Anusha B E

Anusha was born on 4th april 1995, and completed her

secondary education from Royal english public high school

and pre university course from venkatadri independent PU

college and currently pursuing her bachelor degree in

engineering in Atria Institute of Technology, Bangalore.

Lalitha S

Lalitha was born on 22th September 1994, and completed her

secondary education from Gnana Bodha Vidhya Samsthe.

and pre university course from BGS PU College and



Abhilasha A S

Abhilasha was born on 10th July 1994, and completed her

secondary education from government high school, Alur and

diploma from government polytechnic, Chamarajanagar and



http://www.face-rec.org/

attendance monitoring using real time face...

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