Artificial Intelligence Face recognition attendance system using MATLAB

Department of Computer Applications, The M.S. University, Vadodara 1
THE MAHARAJA SAYAJIRAO UNIVERSITY OF BARODA
FACULTY OF SCIENCE
PROJECT REPORT
on
Face Recognition Attendance System
Submitted by
Naomi Kulkarni
Seat No: 2015033800117362
In fulfilment for the award of the degree
of
BACHELOR OF COMPUTER APPLICATIONS
in
The Department of Computer Applications
December, 2017
Guide by: Team members:
Ami Parikh Amey Mohite -2015033800117072
Kshitij Tripathi Shivani Sharma -2015033800117114
Naomi Kulkarni -2015033800117362
Jigar Patel -2015033800117911

Abstract
Uniqueness or individuality of an individual is his face. In this project, face of an individual is used for the
purpose of attendance making automatically. Attendance of the student is very important for every college,
universities and school.
Conventional methodology for taking attendance is by calling the name or roll number of the student and the
attendance is recorded.
Time consumption for this purpose is an important point of concern. Assume that the duration for one
subject is around 60 minutes or 1 hour & to record attendance takes 5 to 10 minutes. For every tutor this is
consumption of time. To stay away from these losses, an automatic process is used in this project which is
based on image processing.
In this project face detection and face recognition is used. Face detection is used to locate the position of
face region and face recognition is used for marking the understudy’s attendance. The database of all the
students in the class is stored and when the face of the individual student matches with one of the faces
stored in the database then the attendance is recorded.

Acknowledgement
We would like to take the opportunity to express our sincere thanks to our guide Ami Parikh and Kshitij
Tripathi for their invaluable support and guidance throughout our project research work. Without their kind
guidance and support this was not possible.
We are grateful to them for their timely feedback which helped us track and schedule the process effectively.
Their time, ideas and encouragement that they gave has helped us to complete our project efficiently.
We would also like to thank Prof. S.R. Srivastava Head of Department for his encouragement and for
providing an outstanding academic environment, also for providing the adequate facilities.
We are thankful to all our teachers for providing advice and valuable guidance.
We also extend our sincere thanks to all the faculty members and the non-teaching staff.

Contents
1.0 Introduction.....................................................................................................................10
1.1 Background Introduction...............................................................................................10
1.2 Using Biometrics ...........................................................................................................10
1.3 Motivation......................................................................................................................10
1.4 Current Systems.............................................................................................................10
1.5 The Problems with Current System...............................................................................11
1.6 Drawbacks in existing system .......................................................................................11
2.0 Proposed Solution ...........................................................................................................12
2.1 Proposed System Components ......................................................................................12
2.2 Proposed System Outcome............................................................................................12
3.0 What contribution would the project make? ...............................................................13
4.0 Project Charter ...............................................................................................................14
4.1 Project Definition...........................................................................................................14
4.2 Project Scope .................................................................................................................14
4.3 Team Size.......................................................................................................................14
5.0 Project Plan .....................................................................................................................15
5.1 Objectives ......................................................................................................................15
5.2 Goals ..............................................................................................................................15
5.3 Software Model - Waterfall Model................................................................................15
5.4 Strategies........................................................................................................................16
5.5 Work Division (w.r.t time) ............................................................................................18
5.6 Work Division (w.r.t team members)............................................................................19
5.7 Tools and Technologies.................................................................................................19
6.0 System Requirements Specification ..............................................................................20
7.0 System Analysis...............................................................................................................22
7.1 System Flow ..................................................................................................................22
7.1.1 Student Registration Flow.......................................................................................22
7.1.2 Taking Attendance Flow .........................................................................................23
7.1.3 Generating Attendance Flow...................................................................................24
7.2 System Architecture.......................................................................................................25
7.3 System Description........................................................................................................26

7.4 System Algorithm..........................................................................................................26
7.5 Module Specification.....................................................................................................28
7.5.1 Face Detection.........................................................................................................28
7.5.2 Face Recognition.....................................................................................................34
7.6 Data Dictionary..............................................................................................................39
7.7 Data Flow Diagrams ......................................................................................................39
8.0 Database Design ..............................................................................................................43
9.0 System Screenshots and Explaination...........................................................................44
10.0 Testing..............................................................................................................................52
10.1 Testing Plan ...................................................................................................................52
10.2 Testing Methods ............................................................................................................52
10.3 Test Cases ......................................................................................................................55
10.4 Testing Reports..............................................................................................................60
11.0 Features of the System....................................................................................................65
12.0 Security Features.............................................................................................................65
13.0 Benefits.............................................................................................................................66
14.0 Limitations.......................................................................................................................66
15.0 Future Enhancement ......................................................................................................67
16.0 Experience and Learning ...............................................................................................68
17.0 Reference and Bibliography...........................................................................................69
List of Figures
Figure 1 Waterfall Model....................................................................................................... 15
Figure 2 Gantt chart ............................................................................................................... 18
Figure 3 Student Registration Flow........................................................................................ 22
Figure 4 Taking Attendance Flow.......................................................................................... 23
Figure 5 Feature Extraction.................................................................................................... 24
Figure 6 Generating Attendance Flow.................................................................................... 24
Figure 7 System Architecture ( Training Phase : Registration ).............................................. 25
Figure 8 System Architecture ( Testing Phase : Attendance )................................................. 25
Figure 9 System Architecture ( Recognition Phase ) .............................................................. 25
Figure 10 Integral image ....................................................................................................... 31

Figure 11 Calculation of Integral image................................................................................. 31
Figure 12 Types of Haar Features .......................................................................................... 31
Figure 13 Haar features applied on an image ......................................................................... 32
Figure 14 AdaBoost feature selection, (a) Haar like features (b) AdaBoost done on images .. 32
Figure 15 Cascade Classifier.................................................................................................. 33
Figure 16 Principal Component Analysis............................................................................... 36
Figure 17 DFD : Context Level.............................................................................................. 39
Figure 18 DFD : Level 0 ........................................................................................................ 40
Figure 19 DFD : Level 1 ( 1.0 Student Registration )............................................................. 41
Figure 20 DFD : Level 1 ( 3.0 Face Detection ) ..................................................................... 41
Figure 21 DFD : Level 1 (2.0 Image Acquisition )................................................................. 41
Figure 22 DFD : Level 1 ( 4.0 Face Recognition ) ................................................................. 42
Figure 23 Database Design .................................................................................................... 43
Figure 24 System Login Interface .......................................................................................... 44
Figure 25 Unsuccessful Login................................................................................................ 44
Figure 26 Home Page............................................................................................................. 45
Figure 27 Registration Interface............................................................................................. 45
Figure 28 Error dialog when existing roll no entered ............................................................. 46
Figure 29 Set camera device and image resolution settings.................................................. 46
Figure 30 Student's image on the axes appears....................................................................... 47
Figure 31 Detected face of the student ................................................................................... 47
Figure 32 Multiple detected faces of the student .................................................................... 48
Figure 33 Folder created by the name of student’s roll no...................................................... 48
Figure 34 Details of registered student................................................................................... 49
Figure 35 Details of registered students ................................................................................. 49
Figure 36 Class Attendance.................................................................................................... 50
Figure 37 Recognized student faces during attendance ( backend process ) ........................... 51
Figure 38 Attendance of students .......................................................................................... 51

Notation
External Entity
External entities are objects outside the system, with which the system communicates. External entities are
sources and destinations of the system's inputs and outputs
Data Flow
Dataflow are pipelines through which packets of information flow. Label the arrows with the name of the data
that moves through it.
Process
Transform of incoming data flow(s) to outgoing flow(s).
Data Store
Data stores are repositories of data in the system. They are sometimes also referred to as files, queue or as
sophisticated as a relational database

Naming and Convention
Principal Component Analysis (PCA)
In simple words, principal component analysis is a method of extracting important variables (in form of
components) from a large set of variables available in a data set.
Local binary patterns (LBP)
It is a type of visual descriptor used for classification in computer vision which is found to be a powerful
feature for texture classification; it has further been determined that when LBP is combined with the
Histogram of oriented gradients (HOG) descriptor, it improves the detection performance considerably on
some datasets.
Eigen Face
Eigenfaces is the name given to a set of eigenvectors when they are used in the computer vision problem of
human face recognition..
Viola–Jones
The Viola–Jones object detection framework is the first object detection framework to provide competitive
object detection rates in real-time. Which can be trained to detect a variety of object classes, it was motivated
primarily by the problem of face detection.
Haar Features
Haar-like features are digital image features used in object recognition. They owe their name to their intuitive
similarity with Haar wavelets and were used in the first real-time face detector.
Adaboost
Adaboost, short for Adaptive Boosting, is a machine learning meta-algorithm which can be used in
conjunction with many other types of learning algorithms to improve performance by 50%.
Classifier
An algorithm that implements classification, especially in a concrete implementation, is known as a classifier.
The term "classifier" sometimes also refers to the mathematical function, implemented by a classification
algorithm that maps input data to a category.

Cascading
Cascading is a particular case of ensemble learning based on the concatenation of several Classifiers, Using all
information collected from the output from a given classifier as additional information for the next classifier in
the cascade.
Linear Discriminant Analysis (LDA)
Linear discriminant analysis (LDA) is a generalization of Fisher's linear discriminant, a method used in
statistics, pattern recognition and machine learning to find a linear combination of features that characterizes
or separates two or more classes of objects or events.
Cascade Object Detector
The Computer Vision System Toolbox cascade object detector can detect object categories whose aspect ratio
does not vary significantly which comes with several pretrained classifiers for detecting frontal faces, profile
faces, noses, eyes, and the upper body.
InsertObjectAnnotation
RGB = insertObjectAnnotation (I, shape, position, label, Name, Value) uses additional options specified by
one or more name-value pair arguments. Example: - insertObjectAnnotation (I, ‘rectangle', position, label)
inserts rectangles and labels at the location indicated by the position matrix.
Computer Vision Toolbox
Design and simulate computer vision and video processing systems which provides algorithms, functions, and
apps for designing and simulating computer vision and video processing systems.
Image Acquisition Toolbox
Image Acquisition Toolbox enables you to acquire images and videos from cameras and frame grabbers
directly into MATLAB and Simulink.
Image Processing Toolbox
Image Processing Toolbox provides a comprehensive set of reference-standard algorithms and workflow apps
for image processing, analysis, visualization, and algorithm development.

1.0 Introduction
1.1 Background Introduction
The current method that colleges use is that the faculty passes a sheet or make roll calls and mark
the attendance of the students and this sheet further goes to the admin department with updates in the final
excel sheet. This process is quite hectic and time consuming. Also, for professors or employees at institutes or
organizations the biometric system serves one at a time. So, why not shift to an automated attendance system
which works on face recognition technique? Be it a class room or entry gates it will mark the attendance of
the students, professors, employees, etc.
1.2 Using Biometrics
Biometric Identification Systems are widely used for unique identification of humans mainly for
verification and identification. Biometrics is used as a form of identity access management and access
control. So use of biometrics in student attendance management system is a secure approach. There are
many types of biometric systems like fingerprint recognition, face recognition, voice recognition, iris
recognition, palm recognition etc. In this project, we used face recognition system.
1.3 Motivation
The main motivation for us to go for this project was the slow and inefficient traditional manual
attendance system. These made us to think why not make it automated fast and much efficient. Also such
face detection techniques are in use by department like crime investigation where they use CCTV footages and
detect the faces from the crime scene and compare those with criminal database to recognize them.
1.4 Current Systems
At present attendance marking involves manual attendance on paper sheet by professors and teachers
but it is very time consuming process and chances of proxy is also one problem that arises in such type of
attendance marking. Also there are attendance marking system such as RFID (Radio Frequency
Identification), Biometrics etc. but these systems are currently not so much popular in schools and
classrooms for students.

1.5 The Problems with Current System
The problem with this approach in which manually taking and maintains the attendance record is that
it is very inconvenient task. Traditionally, student attendances are taken manually by using attendance sheet
given by the faculty members in class, which is a time consuming event. Moreover, it is very difficult to verify
one by one student in a large classroom environment with distributed branches whether the authenticated
students are actually responding or not. The ability to compute the attendance percentage becomes a major
task as manual computation produces errors, and also wastes a lot of time. This method could easily allow
for impersonation and the attendance sheet could be stolen or lost.
1.6 Drawbacks in existing system
¬ These attendance systems are manual
¬ There is always a chance of forgery (one person signing the presence of the other one) Since these
are manual so there is great risk of error
¬ More man-power is required (some person to take attendance)
¬ Calculations related to attendance are done manually (total classes attended in month) which is prone
to error. It is difficult to maintain database or register in manual systems
¬ It is more costly (price of register, pen and the salary of person taking attendance)
It is difficult to search for particular data from this system (especially if that data we are asking for is
of every long ago)
The previous approach in which manually takes and maintains the attendance records was very
inconvenient task. Traditionally, student’s attendances are taken manually by using attendance sheet given
by the faculty members in class, which is a time consuming event. Moreover, it is very difficult to verify one
by one student in a large classroom environment with distributed branches whether the authenticated students
are actually responding or not.
The ability to compute the attendance percentage becomes a major task as manual computation
produces errors, and also wastes a lot of time. This method could easily allow for impersonation and the
attendance sheet could be stolen or lost.

2.0 Proposed Solution
To overcome the problems in existing attendance system we shall develop a Biometric based
attendance system over simple attendance system. Interactive system over static one. Digitized attendance
system over file system. There are many solutions to automate the attendance management system like
thumb based system, simple computerized attendance system but all these systems have limitations over
work and security point of view. Our proposed system shall be a “Face Recognition Attendance System”
which uses the basic idea of image processing which is used in many secure applications like banks, airports
etc.
2.1 Proposed System Components
Following are the main components of the proposed system
1. Student Registration
2. Face Detection
3. Face Recognition
 Feature Extraction
 Feature Classification
4. Attendance management system
It will allow uploading, updating and deletion of the contents of the system.
Attendance management will handle:
 Automated Attendance marking
 Manual Attendance marking
 Attendance details of users.
2.2 Proposed System Outcome
¬ System will only allow authenticated user to login to the system and/or make changes to it.
¬ It will allow user to mark attendance of the students via face recognition technique.
¬ It will detect faces via webcam and then recognize the faces.
¬ After recognition it will mark the attendance of the recognized student and update the attendance
record.
¬ The user will be able to print these record details afterward.

3.0 What contribution would the project make?
Face recognition is the most natural biological features recognition technology according to the
cognitive rule of human beings; its algorithm is ten times more complex than a fingerprint algorithm.
Face recognition is featured by the following advantages compared to fingerprint:
1. Accurate and Fast Identification
Industrial Leading Facial Recognition Algorithm, match more data than
fingerprint, FAR<0.0001%
2. High Usability and Security
Failure to enrol and acquire rate is less than 0.0001%, fingerprint technology will have
problems for enrolment with cold, wet, desquamation, elder, and around 5% people cannot get
enrolled with fingerprint technology
Incident track able for security with photo which captured by camera, there is no evidence
with fingerprint technology to track the incident.
3. User friendly design
Contact less authentication for ultimate hygiene.

4.0 Project Charter
4.1 Project Definition
Face Recognition is a biometric method of identifying an individual by comparing live capture or
digital image data with the stored record for that person.
Face Recognition Attendance System is marking of attendance based on this technology.
4.2 Project Scope
¬ Provides an automated attendance system that is practical, reliable and eliminate disturbance and
time loss of traditional attendance systems.
¬ Present a system that can accurately evaluate student’s performance depending on their recorded
attendance rate.
4.3 Team Size
Our project team consist of four students namely :
1. Amey Mohite
2. Jigar Patel
3. Naomi Kulkarni
4. Shivani Sharma

5.0 Project Plan
5.1 Objectives
¬ Detection of unique face image amidst the other natural component such as walls and other
backgrounds.
¬ Detection of faces amongst other face characters such as beard, spectacles etc.
¬ Extraction of unique characteristic features of a face useful for face recognition.
¬ Effective recognition of unique faces in a class (individual recognition).
¬ Automated update in the attendance sheet without human intervention.
5.2 Goals
¬ To help the lecturers, improve and organize the process of track and manage student attendance.
¬ Provides a valuable attendance service for both teachers and students.
¬ Reduce manual process errors by provide automated and a reliable attendance system.
¬ Increase privacy and security which student cannot present him or his friend while they are not.
¬ Produce monthly reports for lecturers.
¬ Flexibility, Lectures capability of editing attendance records.
5.3 Software Model - Waterfall Model
Waterfall Model is a sequential approach, where each fundamental activity of a process represented
as a separate phase, arranged in linear order.
In the waterfall model, you must plan and schedule all of the activities before starting, working on
them (plan-driven process).
Figure 1 Waterfall Model

5.4 Strategies
¬ Finalize Project Details
 Find information regarding our project.
 Gathering more detail about what we required to learn.
 What are our expectations from project?
 Listing the scope of the project.
 Develop a detailed plan and define goals.
 Creating measurable criteria for success.
 Listing roles and responsibilities of team members
¬ Set Clear Expectations
 Checking that we have everything which can lead us to a successful project.
 Being clear about which team members are responsible for all the components of a project.
 Work contribution makes accountability clear, what member should perform which task.
 Conveying information and explaining what we are actually going to do on project to the teacher
in the project proposal.
¬ Choose the Right Team and System
 First of all we choose the team members as our plans and expectation were clear.
 Distributing work according skills, talent and personalities that are right for each particular
project module.
 Tried to avoid having too many people on a team as well, so the communication goes easy.
¬ Define Milestones
 It is important to define key milestones throughout the lifecycle of the project.
 We started by including four main phases: initiation, planning, execution, and closure.
 Performing an evaluation after each phase to know how our team is doing by examining
deliverables.
 This process keeps you informed about any problems that arise while ensuring that each phase of
the project is completed successfully.

¬ Establish Clear Communication
 The element that can make or break a project is communication, we have focused on it and tried
to make it fluid.
 Time to time sharing the progress report to each other.
 Sharing of any major issues and discussing it together.
¬ Manage Project Risks
 There is risk in every project, so evaluating it at the start and manage it.
 Keeping backup plans ready for preventing stuck situation in project.
 Completing modules of decided time so we get enough time to test every functionality perfectly
and if any problem arises we get sufficient time to solve it.
¬ Avoid Scope Creep
 Its duty of everyone to keep project on track.
 It is important to know how much change can occur before affecting deadlines and deliverables.
 Scope creep generally takes place when there are additions to a project, which is not revised
accordingly.
¬ Evaluate the Project after Completion
 Each project provides information that you can utilize in the future.
 This is why reviewing the project as a whole is such a valuable practice.
 When project members know what went right, what went wrong, and how to make adjustments
next time, they are able to develop best practices for future work.

5.5 Work Division (w.r.t time)
Figure 2 Gantt chart

5.6 Work Division (w.r.t team members)
Amey Mohite & Jigar Patel
¬ Graphical User Interface
¬ Registration
¬ Face Recognition
¬ Generating attendance in Excel Sheet
Naomi Kulkarni & Shivani Sharma
¬ Face Detection
¬ Database Storage
¬ Face Recognition
¬ Registered Students in Excel sheet
5.7 Tools and Technologies
Tools
¬ MATLAB
¬ Microsoft Excel
¬ www.lucidchart.com
¬ www.teamgantt.com
Technologies
MATLAB
 Computer vision toolbox
 Image acquisition toolbox
 Image processing toolbox
 Parallel Computing toolbox

6.0 System Requirements Specification
I. Technical Requirement
i. Hardware Requirements
¬ A standalone computer
¬ High quality camera must be positioned in the class room to obtain the snapshots.
¬ Secondary memory to store all the images and database
ii. Software requirements
¬ MATLAB Version 8.5.0(R2015a) or higher
¬ Windows 8 or higher
II. Functional Requirements
System functional requirement describes activities and services that must provide.
¬ User must be able to manage student records.
¬ Only authorized user must be able to use the system.
¬ System must be attached to webcam and face recognition should be smooth.
¬ The administrator or the person who will be given the access to the system must login into
system before using it.
¬ The information must be entered and managed properly.
III. Non-Functional Requirements
Non-functional Requirements are characteristics or attributes of the system that can judge its
operation. The following points clarify them:
a. Accuracy and Precision: the system should perform its process in accuracy and
precision to avoid problems.
b. Flexibility: the system should be easy to modify, any wrong should be correct.
c. Security: the system should be secure and saving student's privacy.
d. Usability: the system should be easy to deal with and simple to understand.
e. Maintainability: the maintenance group should be able to cope up with any problem
when occurs suddenly.
f. Speed and Responsiveness: Execution of operations should be fast.

Non–Functional Requirements are as follow:
¬ The GUI of the system will be user friendly.
¬ The data that will be showed to the users will be made sure that it is correct and is available for
the time being.
¬ The system will be flexible to changes.
¬ The system will be extensible for changes and to the latest technologies.
¬ Efficiency and effectiveness of the system will be made sure.
¬ The performance of the system will be made sure.
IV. Student Requirements
¬ Student needs to enter the proper details while registering him/her.
¬ He/ She needs to sit properly and capture 10-15 images of himself/herself in different
direction and expressions.
¬ At the time of taking attendance, students need to sit properly facing the camera.
V. Teaching Staff Requirements
¬ The faculty needs to login the system at the time of attendance.
¬ The faculty needs to enter lecture details before starting the attendance process.
¬ If the entered lecture details doesn’t match with the ones in the database(excel sheet) an error
dialog will be displayed.
¬ As the students are recognized by the system, the attendance report will be generate and
shown to the faculty.
VI. Administrator Requirements
¬ The administrator needs to login the system at the time of registering the students for the face
recognition process.
¬ He / She must make sure that the student enters the details properly.
¬ Only the administrator has the rights to manage any changes in the system.
¬ Only the administrator is allowed to view the Training set and the Testing set.
¬ Only the administrator has the rights to manage any changes in the stored data set.

7.0 System Analysis
7.1 System Flow
7.1.1 Student Registration Flow
Figure 3 Student Registration Flow

7.1.2 Taking Attendance Flow
Figure 4 Taking Attendance Flow

Feature Extraction
Figure 5 Feature Extraction
7.1.3 Generating Attendance Flow
Figure 6 Generating Attendance Flow

7.2 System Architecture
Figure 7 System Architecture ( Training Phase : Registration )
Figure 8 System Architecture ( Testing Phase : Attendance )
Figure 9 System Architecture ( Recognition Phase )

7.3 System Description
The system consists of a camera that captures the images of the classroom and sends it to the image
enhancement module. After enhancement the image comes in the Face Detection and Recognition modules
and then the attendance is marked on the database server.
At the time of enrolment, templates of face images of individual students are stored in the Face
database known as Training Set. Here all the faces are detected from the input image and the algorithm
compares them one by one with the face database (Testing Set). If any face is recognized, the attendance is
marked on the server from where administrator can access and use it for different purposes.
Teachers come in the class and just press a button to start the attendance process and the system
automatically gets the attendance without even the intensions of students and teacher. In this way a lot of
time is saved and this is highly securing process no one can mark the attendance of other.
7.4 System Algorithm
This section describes the software algorithms for the system.
The algorithm consists of the following steps
 Image acquisition
 Histogram normalization
 Skin classification
 Face detection
 Face recognition
 Attendance.
Image Acquisition
Image is acquired from the camera that is connecting above the board.
Histogram Normalization
Captured image sometimes have brightness or darkness in it which should be removed for good results. First
the RGB (colour) image is converted to the gray scale image for enhancement
Colour image is converted to gray scale image for increasing contrast.
Histogram normalization is good technique for contrast enhancement in the spatial domain.

Skin Classification
It is used for increasing the efficiency of the face detection algorithm. Viola Jones algorithm is used for
detection and its accuracy can be increased if the skin is classified before the scanning procedure of faces.
Pixels those are closely related to the skin become white and all other are black. This binary image uses the
thresholding of skin colours.
Face Detection
In this stage faces are detected by marking the rectangle on the faces of the student appearing on the image
source.
After the detection of faces from the window, the next step is cropping of each detected face.
Each cropped image is assigned as a separate thread for the recognition.
Face and recognition and attendance
After the face detection next step is face recognition. This can be done by cropping the detected faces of
students sitting in the class and compare them with the Training Set which stores the multiple images of an
individual student at the time of registration.
In this way, the face of each student is verified one by one and attendance is marked in the excel sheet and
the report is generated and it is viewed to the faculty.

7.5 Module Specification
7.5.1 Face Detection
It is defined as finding the position of the face of an individual. In other word it can be defined as
locating the face region in an image. After detecting the face of human its facial features extracted and has
wide range of application like facial expression recognition, face recognition, observation systems, human PC
interface and so forth.
For the application of face recognition, detection of face is very important and the first step. Only after
detecting face, the face recognition algorithm can be functional. Face detection itself involves some
complexities for example surroundings, postures, enlightenment etc..
There are two types of face detection problems:
1) Face detection in images
2) Real-time face detection.
Most face detection systems attempt to extract a fraction of the whole face, thereby eliminating most of
the background and other areas of an individual's head such as hair that are not necessary for the face
recognition task.
A proper and efficient face detection algorithm always enhances the performance of face recognition
systems. Various algorithms are proposed for face detection such as Face geometry based methods, Feature
Invariant methods, Machine learning based methods. Out of all these methods Viola and Jones proposed a
framework which gives a high detection rate and is also fast. Viola-Jones detection algorithm is efficient for
real time application as it is fast and robust. Hence we chose Viola-Jones face detection algorithm which
makes use of Integral Image and AdaBoost learning algorithm as classifier. We observed that this algorithm
gives better results in different lighting conditions and we combined multiple haar classifiers to achieve a
better detection rates up to an angle of 30 degrees.

7.5.1.1 Face Detection in Images
With static images, this is often done by running a 'window' across the image. The face detection
system then judges if a face is present inside the window. Unfortunately, with static images there is a very
large search space of possible locations of a face in an image. Faces may be large or small and be positioned
anywhere from the upper left to the lower right of the image. Most face detection systems use an example
based learning approach to decide whether or not a face is present in the window at that given instant.
A neural network or some other classifier is trained using supervised learning with 'face' and 'non-face'
examples, thereby enabling it to classify an image (window in face detection system) as a 'face' or 'non-face'.
There is another technique for determining whether there is a face inside the face detection system's
window - using Template Matching. The difference between a fixed target pattern (face) and the window is
computed and threshold. If the window contains a pattern which is close to the target pattern (face) then the
window is judged as containing a face.
It uses a whole bank of fixed sized templates to detect facial features in an image. By using several
templates of different (fixed) sizes, faces of different scales (sizes) are detected. The other implementation of
template matching is using a deformable template. Instead of using several fixed size templates, we use a
deformable template (which is non-rigid) and thereby change the size of the template hoping to detect a face in
an image. A face detection scheme that is related to template matching is image invariants.
Here the fact that the local ordinal structure of brightness distribution of a face remains largely
unchanged under different illumination conditions is used to construct a spatial template of the face which
closely corresponds to facial features. In other words, the average grey-scale intensities in human faces are
used as a basis for face detection. For example, almost always an individual’s eye region is darker than his
forehead or nose. Therefore an image will match the template if it satisfies the 'darker than' and 'brighter than’
relationships.

7.5.1.2 Real-time face detection
It involves detection of a face from a series of frames from a video capturing device. While the
hardware requirements for such a system are far more stringent, from a computer vision stand point, real-time
face detection is actually a far simpler process than detecting a face in a static image. This is because unlike
most of our surrounding environment, people are continually moving.
Since in real-time face detection, the system is presented with a series of frames in which to detect a
face, by using spatiotemporal filtering (finding the difference between subsequent frames), the area of the
frame that has changed can be identified and the individual detected.
Furthermore, exact face locations can be easily identified by using a few simple rules, such as,
1) The head is the small blob above a larger blob -the body
2) Head motion must be reasonably slow and contiguous -heads won't jump around erratically.
Real-time face detection has therefore become a relatively simple problem and is possible even in
unstructured and uncontrolled environments using this very simple image processing techniques and reasoning
rules.
7.5.1.3 Viola Jones for Face Detection
This method has brought together new algorithms and insights to construct a framework for robust and
extremely rapid visual detection. This face detection system is most clearly distinguished from previous
approaches in its ability to detect faces extremely rapidly. Operating on 384 by 288 pixel images, faces are
detected at 15 frames per second on a conventional 700 MHz Intel Pentium III.
In other face detection systems, difference between pixel values of consecutive frames which were
present in video or the colour value of each pixel was considered to achieve high frame rates. The information
in a grey scale image is worked upon to see if high frame rates have been achieved.
There are four main parts in this method:
1. Integral Images
2. Haar-like Features
3. AdaBoost
4. Cascading Classifier

Integral Image:
In an Integral Image the value of pixel ( x , y ) is calculated by adding the value of pixel above and to the left
of ( x , y ).
Figure 10 Integral image
The calculation of sum of the pixels inside any given rectangle is done using only the corner values of the
rectangle.
Haar-Like Features
Haar features are similar to convolution kernels which are used to detect the presence of that feature in
the given image. Feature set is obtained by subtracting sum of all the pixels present below black area from
sum of all the pixels present below white area.
Figure 12 Types of Haar Features
Figure 11 Calculation of Integral image

AdaBoost
Viola Jones algorithm uses 24x24 window as the base window size to start evaluating these features in
any given image. If we consider all possible parameters of the haar features like position, scale and type we
end up calculating about 160,000+ features in this window.
As stated previously there can be approximately 160,000+ feature values within a detector at 24x24
base resolutions which need to be calculated. But it is to be understood that only few set of features will be
useful among all these features to identify a face.
Figure 14 AdaBoost feature selection, (a) Haar like features (b) AdaBoost done on images
In the above image as we can see there is only one Haar feature which can be used to detect nose. So
the other feature which is used in the second image can be ignored as it gives irrelevant feature.
( a )
Relevant features
( b )
Irrelevant featuresHaar features
Figure 13 Haar features applied on an image

Cascading
The basic principle of the Viola-Jones face detection algorithm is to scan the whole image by
consecutively increasing the scanning window size so that the whole image will be covered. Even in an image
there is a probability that it may contain one or more faces due to an excessive large amount of the evaluated
sub-windows would still be negatives (non-faces).So the algorithm should concentrate on discarding non-faces
quickly and spend more on time on probable face regions.
Hence a single strong classifier formed out of linear combination of all best features is not a good to
evaluate on each window because of computation cost. A cascade classifier is composed of stages where each
stage has certain no of features grouped each containing a strong classifier. The job of each stage is used to
determine whether a given sub window is definitely not a face or may be a face. A given sub window is
immediately discarded as not a face if it fails in any of the stage.
Figure 15 Cascade Classifier

7.5.2 Face Recognition
Face recognition module is used for identifying students in the classrooms then process the related
attendance operation.
When images from the face detection module is transferred to database module (Testing Set) and
they are ready to be used, face recognition module will use these image and facial information to try to find a
match with student images from the database module (Training Set) which stores multiple face images of
registered students. If a match is found between image info and database record then this student is checked
as present in the classroom.
There are many face recognition algorithms that are used to recognize a face from an input image.
7.5.2.1 Principal Component Analysis (PCA):
One of the simplest and most effective PCA approaches used in face recognition systems is the so-
called eigenface approach. This approach transforms faces into a small set of essential characteristics,
eigenface, which are the main components of the initial set of learningimages (training set).
Recognition is done by projecting a new image in the eigenface subspace, after which the person is
classified by comparing its position in eigenface space with the position of known individuals.
The advantage of this approach over other face recognition systems is in its simplicity, speed and
insensitivity to small or gradual changes on the face. The problem is limited to files that can be used to
recognize the face. Namely, the images must be vertical frontal views of human faces.
The whole recognition process involves two steps:
1. Feature Extraction process
2. Recognition process
Feature Extraction Procedure
1. The first step is to obtain a set S with M face images. In our example M=25 and each image in
transformed into a vector of size N and placed into a set.
S= {T1, T2, T3 …. Tm}
2. After you have obtained your set, you will obtain the mean image Ψ.

3. Then you will find the difference, Φ between the input image and mean image.
4. In this step, eigenvectors (Eigen face) ui and the corresponding eigenvalues should be calculated.
The eigenvectors (Eigen face) must be normalized so that they are Eigen vectors, i.e. length of 1.
The description of the exact algorithm for determination of eigenvectors and eigenvalues is
omitted here, as it belongs to the arsenal of most math programming libraries.
5. We obtain the covariance matrix C in the following manner.
6. AT
From M eigenvectors (Eigen face) ui, only M' should be chosen which have the highest
eigenvalues. The higher the eigenvalues, the more characteristic features of a face does the particular
eigenvector describe. Eigen face with low eigenvalues can be omitted, as they explain only a small
part of characteristic features of the face. After M' Eigen face ui are determined, the training phase of
algorithm is finished.
Recognition Procedure
A new face is transformed into its Eigen face components. First we compare our input image
with our mean image and multiply their difference with each eigenvector of the L matrix. Each value
would represent a weight and would be saved on a vector Ω.

We now determine which face class provides the best description for the input image. This is
done by minimizing the Euclidean distance.
The input face is considered to belong to a class if εk is below an established threshold θε.
Then the face image is considered to be a known face. If the difference is above the given threshold
but below a second threshold, the image can be determined as an unknown face. If the input
image is above these two thresholds, the image is determined not to be a face.
Figure 16 Principal Component Analysis

7.5.2.2 Linear Discriminate Analysis (LDA):
Linear Discriminant analysis explicitly attempts to model the difference between the classes of data. LDA is a
powerful face recognition technique that overcomes the limitation of Principle component analysis technique by
applying the linear discriminant criterion.
This criterion tries to maximize the ratio of the determinant of the between-class scatter matrix of the projected
samples to the determinant of the within class scatter matrix of the projected samples. Linear discriminant group images
of the same class and separates images of different classes of the images.
Discriminant analysis can be used only for classification not for regression. The target variable may have two or
more categories. Images are projected from two dimensional spaces to c dimensional space, where c is the number of
classes of the images. To identify an input test image, the projected test image is compared to each projected training
image, and the test image is identified as the closest training image.
The LDA method tries to find the subspace that discriminates different face classes. The within- class scatter
matrix is also called intrapersonal means variation in appearance of the same individual due to different lighting and
face expression. The between-class scatter matrix also called the extra personal represents variation in appearance due to
difference in identity.
Linear discriminant methods group images of the same classes and separates images of the different classes. To
identify an input test image, the projected test image is compared to each projected training image, and the test image is
identified as the closest trainingimage.
7.5.2.3 Linear Binary Pattern Histogram (LBPH):
LBP is really a very powerful method to explain the texture and model of a digital image. A face image is first
split into small regions that LBP histograms are extracted and then concatenated in to a single feature vector. This vector
forms an efficient representation of the face area and can be used to measure similarities between images.
Automatic facial expression analysis is a fascinating and challenging problem, and impacts important
applications in several areas such ashuman and computer interaction and data-driven animation.
Deriving a facial representation from original face images is an essential step for successful facial expression
recognition method. In this paper, we evaluate facial representation predicated on statistical local features, Local Binary
Patterns, for facial expression recognition.

Various machine learning methods are systematically examined on several databases. Broad experiments
illustrate that LBP features are effective and efficient for facial expression recognition.
The area binary pattern (LBP) was originally designed for texture description. It’s invariant to monotonic grey-
scale transformations which are essential for texture description and analysis for the reason of computational simplicity
processing of image in real-time is possible. With LBP it’s possible to explain the texture and model of an electronic
digital image. This is completed by dividing a picture into several small regions from which the features are extracted.
These features contain binary patterns that describe the environmental surroundings of pixels in the regions. The
features that are formed from the regions are concatenated into a single feature histogram, which describes to forms a
representation of the image. Images will then be compared by measuring the similarity (distance) between their
histograms.
According a number of studies face recognition utilising the LBP method provides positive results, both with
regards to speed and discrimination performance.
Due to the way the texture and model of images is described, the technique is apparently quite robust against
face images with different facial expressions, different lightening conditions, aging of persons and image rotation. Hence
it is good for face recognition purposes.

7.6 Data Dictionary
Field Name Data Type Length Constraint Description
Roll_no Int 3 Primary key Student roll no
Name Varchar 20 Not null Name of student
Date Date 10 Not null Date of the attendance
Time Time 10 Not null Time of the attendance
Attendance Varchar 7 Present or Absent Attendance of a student
Images .pgm 100 Size must be of 11KB Images of students
7.7 Data Flow Diagrams
Figure 17 DFD : Context Level

Figure 18 DFD : Level 0

Figure 19 DFD : Level 1 ( 1.0 Student Registration )
Figure 21 DFD : Level 1 (2.0 Image Acquisition )
Figure 20 DFD : Level 1 ( 3.0 Face Detection )

Figure 22 DFD : Level 1 ( 4.0 Face Recognition )

8.0 Database Design
Figure 23 Database Design

9.0 System Screenshots and Explaination
The administrator needs to enter the username and password in order to start this
system.
Warning message is displayed if an incorrect username or password is entered
Figure 24 System Login Interface
Figure 25 Unsuccessful Login

Home page of the system which consists of Registration and Attendance button
On clicking the REGISTER button, registration interface is provided.
Figure 27 Registration Interface
Figure 26 Home Page

If you enter a roll which already exist in the database , then the error dialog
appears.
On successful login, the interface to capture images appears.
On clicking the CAMERA ON button, the dialog boxes appear to ask about the
device type and the resolution type
Figure 28 Error dialog when existing roll no entered
Figure 29 Set camera device and image resolution settings

When the capture button is pressed the image has been captured and preview is
shown into other axes highlighting the detected face.
After the camera settings have been selected, the student’s image appears on the axes.
Figure 30 Student's image on the axes appears
Figure 31 Detected face of the student

Multiple faces are captured while registration with different expression for
more accuracy.
A unique new folder containing the detected face images of the student is
created after the registration by the name of student’s roll no in the Training
set.
Figure 32 Multiple detected faces of the student
Figure 33 Folder created by the name of student’s roll no

Figure 34 Details of registered student
Figure 35 Details of registered students
Faces captured during registration are stored in database in the .pgm format and entry
of the student’s name and roll no is done into excel sheet named Registered Students

Figure 36 Class Attendance
On clicking the GENERATE ATTENDANCE button, the recognition process starts and the detected faces are recognized.

Figure 37 Recognized student faces during attendance ( backend process )
Figure 38 Attendance of students

Department of Computer Applications, The M.S. University , Vadodara 52
10.0 Testing
10.1 Testing Plan
A test plan is a great reminder and to-do list. It’s a way to keep track of everything you need to do to
ensure you have the right participants at the right time in the right location with the right setup and the right
set of tasks to perform. Having a test plan is also important when you communicate with the rest of the team.
It’s one document that tells everyone involved what's going on, when, and why. The term "test plan" sounds
quite formal, but really all that your plan is doing is listing out the decisions you've made and the things that
need to happen for the usability test to take place.
This helps you keep track of what's going on and what still needs to be done. Most of the information in
the test plan is stuff that comes from other documents you use during the planning and execution of the
study. For instance, your participant profile, the study schedule, and your task list. You’ll also pull in
information that might not be written down anywhere, such as the research questions that led to your task
list.
10.2 Testing Methods
¬ Unit Testing
Unit testing is the testing of an individual unit or group of related units. It falls under the class of
white box testing.
It is often done by the programmer to test that the unit he/she has implemented is producing expected output
against given input.
¬ Integration Testing
Integration testing is testing in which a group of components are combined to produce output.
Also, the interaction between software and hardware is tested in integration testing if software and hardware
components have any relation.
It may fall under both white box testing and black box testing.
¬ Functional Testing
Functional testing is the testing to ensure that the specified functionality required in the system
requirements works.
It falls under the class of black box testing.

¬ System Testing
System testing is the testing to ensure that by putting the software in different environments (e.g.,
Operating Systems) it still works.
System testing is done with full system implementation and environment. It falls under the class of black
box testing.
¬ Stress Testing
Stress testing is the testing to evaluate how system behaves under unfavourable conditions.
Testing is conducted at beyond limits of the specifications. It falls under the class of black box testing.
¬ Performance Testing
Performance testing is the testing to assess the speed and effectiveness of the system and to make
sure it is generating results within a specified time as in performance requirements.
¬ Usability Testing
Usability testing is performed to the perspective of the client, to evaluate how the GUI is user-
friendly? How easily can the client learn? After learning how to use, how proficiently can the client
perform? How pleasing is it to use its design? This falls under the class of black box testing.
¬ Acceptance Testing
Acceptance testing is often done by the customer to ensure that the delivered product meets the
requirements and works as the customer expected.
¬ Regression Testing
Regression testing is the testing after modification of a system, component, or a group of related
units to ensure that the modification is working correctly and is not damaging or imposing other modules to
produce unexpected results. It falls under the class of black box testing.

¬ Beta Testing
Beta testing is the testing which is done by end users, a team outside development, or publicly
releasing full pre-version of the product which is known as beta version.
The aim of beta testing is to cover unexpected errors. It falls under the class of black box testing.
¬ Black box testing
Black box testing is a testing technique that ignores the internal mechanism of the system and
focuses on the output generated against any input and execution of the system.
It is also called functional testing.
¬ Software testing
Software testing is the process of evaluation a software item to detect differences between given
input and expected output. Also to assess the feature of A software item. Testing assesses the quality of the
product. Software testing is a process that should be done during the development process.
In other words software testing is a verification and validation process.
¬ Verification
Verification is the process to make sure the product satisfies the conditions imposed at the start of the
development phase.
In other words, to make sure the product behaves the way we want it to.
¬ Validation
Validation is the process to make sure the product satisfies the specified requirements at the end of
the development phase.
In other words, to make sure the product is built as per customer requirements.

10.3 Test Cases
ID TC01
TITLE Login
PREREQUISITE
Username
Password
TEST ACTION
1. Start the application
2. Fill in the Login details
EXPECTED
RESULT
It checks whether the username and password are correct
or not and provide access to home page or else it will
display an error dialog
ID TC02
TITLE Registration
PREREQUISITE
Student Roll no
Student Name
TEST ACTION
2. Login
3. Click registration button
4. Fill registration form
5. Click register button
EXPECTED
RESULT
System checks the inputted form of data is correct , to
which on clicking the register button it provides access to
another phase of registration and an entry of the student’
roll no and name is generated in an excel sheet else error
dialog will be shown in case of incorrect data.

ID TC03
TITLE Web Camera Integration and configuration
PREREQUISITE Web cam is connected and integrated with the system
TEST ACTION
2. Log In
3. Click Registration
4. Fill Registration Form
5. Click on Camera On button
6. Select Camera Device ID
7. Select type of Dimension of camera
EXPECTED
RESULT
System integrates with the web camera and turns ON
inside the specified MATLAB axis figure

ID TC04
TITLE Face Detection and storage in Training set
PREREQUISITE
Faces are detected from the captured image , cropped and
stored in Training set
TEST ACTION
2. Log In
3. Click Registration
4. Fill Registration Form
8. Click on Capture button
EXPECTED
RESULT
On clicking capture button, an image is captured,
displayed in second axis with the box bordered on the
detected face, this detected face is cropped and in Training
set database, subfolder is created of that Roll number
which was inputted by the student and within it captured
image of student is stored of .pgm format

ID TC05
TITLE Multiple Face Detection and storage in Testing set
PREREQUISITE
Faces are detected from the captured image, cropped and
stored in Testing set
TEST ACTION
2. Log In
3. Click Mark Attendance
7. Click on Capture
EXPECTED
RESULT
Multiple images are detected, displayed inside the second
axis of the window with the square bordered on the
detected faces, cropped and stored in Testing set.

ID TC06
TITLE
Face Recognition and generation of attendance in an Excel
sheet
PREREQUISITE
Faces within the Testing set are recognized with the
Training set and accordingly entry is generated in Excel
sheet
TEST ACTION
2. Log In
3. Click Mark Attendance
7. Click on Capture
8. Click on Generate
EXPECTED
RESULT
System checks the inputted form of data is correct , to
which on clicking the register button it provides access to
another phase of registration and an entry of the student’
roll no and name is generated in an excel sheet else error
dialog will be shown in case of incorrect data.

10.4 Testing Reports
TEST ID TC01
ITERATION EXPECTED RESULT ACTUAL RESULT STATUS
1
It checks whether the
username and password
is correct or not and
provide access to home
page and warning
message respectively
It checks whether the
username and password
is correct or not and
provide access to home
page and warning
message respectively
Pass
TEST ID TC02
1
System checks the
inputted data of the
form is in the correct
form, to which on
submit it provides
access to another part of
registration and an entry
is generated in an Excel
sheet else prompt
message is shown for an
incorrect data
System checks the
inputted data of the form
is in the correct form, to
which on submit it
provides access to
another part of
registration and an entry
is generated in an Excel
sheet else prompt
message is shown for an
incorrect data
Pass

TEST ID TC03
1
Inbuilt camera integrates
with the system and
turns ON inside the
specified MATLAB axis
figure
Inbuilt camera integrates
with the system and turns
ON inside the specified
MATLAB axis figure
Pass

TEST ID TC04
1
On clicking capture button,
an image is captured,
displayed in second axis
with the box bordered on
the detected face, this
detected face is cropped
and in Training set
database, subfolder is
created of that Roll number
which was inputted by the
student and within it
captured image of student
is stored of .pgm format
On clicking capture
button, an image is
captured, displayed in
second axis with the box
bordered on the detected
face, this detected face is
cropped and in Training
set database, subfolder is
created of that Roll
number which was
inputted by the student
and within it captured
image of student is stored
of .pgm format
Pass
2
On clicking capture button,
an image is captured,
displayed in second axis
with the box bordered on
the detected face, this
detected face is cropped
and in Training set
database, subfolder is
created of that Roll number
which was inputted by the
student and within it
captured image of student
is stored of .pgm format
Faces are not detected due
to bad light or more
brightness or low quality
camera and if distance is
more between the person
and the camera
Fail

TEST ID TC05
1
Multiple images are
detected, shown in second
axis of the window with
the square bordered on the
detected faces, cropped all
and stored in Testing set
Multiple images are
Pass
2
Multiple images are
Faces are not detected due
to bad light or more
brightness or low quality
camera and if distance is
more between the person
and the camera
Fail

TEST ID TC06
1
Faces are recognized, if
matched, entry is made in
Excel sheet of that
particular roll number as
present if not then absent
Faces are recognized,
Excel sheet of that
Pass
2
Faces are recognized, if
Excel sheet of that
Faces are recognized with
the wrong person
Fail

11.0 Features of the System
¬ Automatically identify or verify a person from a digital image from a video source.
¬ Reduces cost and time
¬ This system is digitized
¬ It is more secure than manual one (face recognition)
¬ It uses biometrics which is even more secure method than simple security systems
(password oriented)It uses less man power i.e. it does not require a person to take
attendance
¬ It can store more databases.
¬ It gives calculations more easily and in less time
12.0 Security Features
In our project there is no external database or server base database.( AT&T database
concept is been used to store in the form of training sets and testing set as well as our
attendance information. )
This database is generated itself in the computer system, it has a hierarchical
architecture of folders to save the data from our Attendance system.
It can be made secure by giving folders admin rights and locking it with passwords.
So an unauthorized user would be able to access the internal data of the system and
make changes.

13.0 Benefits
¬ The system stores the faces that are detected and automatically marks
attendance. Provide authorized access.
¬ Ease of use.
¬ Multiple face detection.
¬ Used for secure purposes.
¬ It saves their time and efforts.
¬ The software stores the faces that are detected and automatically marks
attendance.
¬ The system is convenient and secure for the user.
14.0 Limitations
¬ Expensive
¬ Difficulties with big data processing and storing
¬ Strong influence of the camera angle, lighting and image quality
¬ Fooled by identical twins

15.0 Future Enhancement
The project "FACE RECOGNITION ATTENDANCE SYSTEM" has been done
taking into consideration all the factors of SRS and without any major
redundancy. There are some aspects which can be further modified in future if
the changes are required. The enhancements may be like we can modify
recognition algorithms into 3D live streaming using neural network and Machine
Language for higher accuracy and with SMS notifications to send details of
attendance to the respective mobile numbers of students.

16.0 Experience and Learning
From scratch to working software, Carrying out real-world software projects in our academic
studies helps us to understand what we have to face in industry.
And working in a team collaboratively taught us how to work as a team together as almost
every software project is done by group of people working together to achieve single goal.
We have learned most of the industrial strategies used for completion of project by keeping
accounts of time, quality, and budget.
It was a wonderful experience working on "Face Recognition Attendance System" with
enthusiastic and like-minded people wherein we explored a part of Artificial Intelligence, i.e.
image processing, which relates to our system from capturing images, detecting faces,
storing them in a database, extracting the facial features, recognizing them and generating
attendance through different algorithms, books, websites and with the guidance of our guide.
This project was a door to a Stairs of Success towards the bright Software Engineering
career.

17.0 Reference and Bibliography
https://www.coursera.org/learn/image-processing
https://www.scribd.com/doc/23257079/Introduction-to-Matlab-Image-Processing-By-Dhananjay-K-Theckedath
https://en.wikipedia.org/wiki/Facial_recognition_system
https://stackoverflow.com/questions/tagged/face-recognition
https://www.quora.com/topic/Face-Detection
Face Detection and Recognition: Theory and Practiceby Asit Kumar Datta and Pradipta Kumar Banerjee
G. Yang and T. S. Huang, “Human face detection in complex background,” Pattern
Recognition Letter, vol. 27, no.1, pp. 53-63, 1994
Heisele, B. and Poggio, T. (1999) Face Detection. Artificial Intelligence Laboratory. MIT
Narayanaswamy, C.R. and Raghavarao, D. (1991) Principal component analysis of large
Dispersion matrices. APSTAG 40:2. pp309-316
Turk, M. and Pentland, A. (1991a). Eigenfaces for recognition. Journal of Cognitive
Neuroscience, 3(1), 71-86
K.Senthamil Selvi, P.Chitrakala, A.Antony Jenitha, “Face recognition based Attendance marking system” IJCSMC,
Vol. 3, Issue. 2, February 2014, pg.337 – 342
Mr.C.S.Patil, Mr.R.R.Karhe, Mr.M.D.Jain, “Student Attendance Recording System Using Face Recognition with
GSM Based” International Journal of Research in Advent Technology, Vol.2, No.8, August 2014 E-ISSN: 2321-9637
Mathana Gopala Krishnan, Balaji, Shyam Babu, “Implementation of Automated Attendance System using Face
Recognition” International Journal of Scientific & Engineering Research, Volume 6, Issue 3, March-2015

Artificial Intelligence Face recognition attendance system using MATLAB

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