ATTENDANCE MONITORING USING REAL TIME FACE...
Transcript of ATTENDANCE MONITORING USING REAL TIME FACE...
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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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ISSN: 2278 – 909X International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE)
Volume 5, Issue 5, May 2016
1309
All Rights Reserved © 2016 IJARECE
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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ISSN: 2278 – 909X International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE)
Volume 5, Issue 5, May 2016
1310
All Rights Reserved © 2016 IJARECE
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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ISSN: 2278 – 909X International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE)
Volume 5, Issue 5, May 2016
1311
All Rights Reserved © 2016 IJARECE
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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ISSN: 2278 – 909X International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE)
Volume 5, Issue 5, May 2016
1312
All Rights Reserved © 2016 IJARECE
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
currently pursuing her bachelor degree in engineering in
Atria Institute of Technology, Bangalore.
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
currently pursuing her bachelor degree in engineering in
Atria Institute of Technology, Bangalore.