project synopsis face recognition attendance system

“FACE RECOGNITION IN E-
ATTENDANCE”
Submitted in the partial fulfillment of requirements for the award of Degree of Bachelor of
Technology in Computer Science & Engineering
Session: 2022-23
Submitted To
DR. ABDUL KALAM TECHNICAL UNIVERSITY
UTTAR PRADESH
Submitted By
Akash Kumar 2000460109001
Ankit Rao 2000460109002
Akanksha 1900460100009
Divya Yadav 1900460100040
Under the guidance of
Prof.: Mr. Milind Sir
HOD: Dr. Saurabh Sir
DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING
Maharana Pratap Engineering College
Kanpur

1. Project Title
2. Introduction
3. Problem statement
4. Objective
5. Feasibility study
6. Methodology
7. Module specification
8. E-R diagram
9. Data flow diagram
10. Facility required for purposed work
11. Tools & Environment are used
12. Conclusion
13. Future scope
14. Reference or Bibliography
Table of Contents

Introduction
Attendance systems of old practices are not quite efficient now a days for keeping track on
student’s attendance. Student enrollment in schools and colleges increasing every year and
taking each student attendance plays a very vital role. So, it is necessaryto discuss theeffective
system which records the attendance of a student automatically.
Maintaining the attendance is very important in all the schools/colleges for checking the
performance of students. Every school/college has its own method in this regard. Some are
taking attendance of students manually using attendance registers or marking attendance
sheets or file-based approach and some have adopted the methods of automatic attendance
using some biometric techniques. But in these methods, students have to wait for a long time
in making a queue at the time they enter inside the classroom.
Many biometric systems are available in the market but the key authentications are same in
all of the techniques. Every biometric system consists of enrollment process in which the
unique features of a person is stored in the database and after that, there are some processes of
identification and verification of the person.The purpose of developing the new attendance
management system is to computerize the traditional methods of taking the attendance.
Therefore, in order to drag the attention of students and make them interactive in observing
technologies, we try to move on to the latest upcoming trends on developing attendance
systems. This is the reason for college/school attendance management system to come up
with an approach that ensures a strong contribution of students in classrooms.
To track the attendance of the students, we have introduced the attendance management
system. With the introduction of this attendance system, skipping classes for students without
the staff’s knowledge have become difficult. Attendance management system is to count the
number ofstudents and urge students to attend the classes on time, so as to improve the quality
of teaching.
wavelet, Auto-Correlation etc. of the face. However, the face recognition still remains a
challenging problem for us because of its fundamental difficulties regarding various factor
like illumination changes, face rotation, facial expression etc.
Problem Statement
When it here is so many students in a school/college, it becomes more and more difficult to
mark attendance for each student and it is time consuming too. The Existing system of any
institute is a manual entry for the students. This system faces the issue of wastage of time and
also becomes complicated when the strength is more than the usual. Here, the attendance is
being carried out in the hand written registers. It is very tedious job for us to maintain the
record of the user.

Whenever we have to measure the performance of students, finding and calculating the
average of the attendance of each enrolled student is also a very complicated task for us. The
human effort is more here. The retrieval of the information is not a piece of cake as the records
are maintained in the hand written registers. Let us suppose that the wrong inputs are entered,
the application resist to work. So, the user finds it difficult to use the existing system.
The paper proposed by Zhao, W et al. (2003) has listed the difficulties of facial identification.
One of the difficulties of facial identification is the identification between known and
unknown images. In addition, paper proposed by Pooja G.R et al. (2010) found out that the
training process for face recognition student attendance system is slow and time-consuming.
In addition, the paper proposed by Priyanka Wagh et al. (2015) mentioned that different
lighting and head poses are often the problems that could degrade the performance of face
recognition-based student attendance system.
Hence, there is a need to develop a real time operating student attendance system which means
the identification process must be done within defined time constraints to prevent omission.
The extracted features from facial images which represent the identity of the students have to
be consistent towards a change in background, illumination, pose and expression. High
accuracy and fast computation time will be the evaluation points of the performance.
Objective
Our objective is to detect unique faces with the help of mobile camera amidst the other natural
components like walls, backgrounds etc. and to extract the unique features faces amongst other
face characteristics such as beard, spectacles etc. of a face useful for face detection and
recognition.
Our primary goal is to help the lecturers, improve and organize the process of track and
manage student attendance and absenteeism. Additionally, we seek to:
 Provides a valuable attendance service for both teachers and students.
 Reduce manual process errors by provide automated and a reliable attendance system
uses face recognition technology.
 Increase privacy and security which student cannot presenting himself or his friend
while they are not.
 Produce monthly reports for lecturers.
 Flexibility, Lectures capability of editing attendance records.
 Calculate absenteeism percentage and send reminder messages to students.
 Easily manageable by School/College Staff and convert in the form of excel sheet.


Feasibility study
A feasibility study is a high-level capsule version of the entire System analysis and Design
Process. The study begins by classifying the problem definition. Feasibility is to determine if
it’s worth doing. Once an acceptance problem definition has been generated, the analyst
develops a logical model of the system. A search for alternatives is analyzed carefully.
There are 3 parts in feasibility study.

Operational Feasibility
Question that going to be asked are:
 Will the system be used if it developed and implemented?
 If there was sufficient supportfor the project from the management and from the
users.

 Have the users been involved in planning and development of the Project.
 Will the system producepoorerresult in any respect or area?
This systemcan be implemented in the organization because there is adequate supportfrom
management and users. Being developed in Python so that the necessary operations are
carried out automatically.

Technical feasibility:
 Does the necessary technology exist to do what is been suggested?
 Does the proposed equipment have the technical capacity for using the new system?
 Are there technical guarantees of accuracy, reliability and data security?
 The project is developed on Pentium version processor with 256 – 1024 MB RAM.
 The environment required in the development of system is any windows platform. The
observer pattern along with factory pattern will update the results eventually
 The language used in the development is corejava Programming & Windows
Environment
Financial and Economic Feasibility:
The systemdeveloped and installed will be good benefit to the organization. The system will
be developed and operated in the existing hardware and software infrastructure. So, there is
no need of additional hardware and software for the system.

Methodology
The approach performs face recognition-based student attendance system. The methodology
flow begins with the capture of image by using simple and handy interface, followed by pre-
processing of the captured facial images, then feature extraction from the facial images,
subjective selection and lastly classification of the facial images to be recognized. Both LBP
and PCA feature extraction methods are studied in detail and computed in this proposed
approach in order to make comparisons. LBP is enhanced in this approach to reduce the
illumination effect. An algorithm to combine enhanced LBP and PCA is also designed for
subjective selection in order to increase the accuracy. The details of each stage will be
discussed in the following sections.
The flow chart for the proposed system is categorized into two parts, first training of images
followed by testing images (recognize the unknown input image) shown in Figure 1 and Figure
2 respectively.
Fig 1: Flow of proposed Approach (training part)

Fig 2: Flow of Proposed Approach (recognition parts)

Modules specification
The Proposedsystemare divided into Five modules such as:
 Input image/ Image Capture
 Face detection
 Pre-Processing

 Database development
 Post processing
Input Image
Although our own database should be used to design real time face recognition student
attendance system, the databases that are provided by the previous researchers are also used
to design the system more effectively, efficiently and for evaluation purposes.
Yale face database is used as both training set and testing set to evaluate the performance.
Yale face database contains one hundred and sixty-five grayscale images of fifteen
individuals. There are eleven images per individual; each image of the individual is in different
condition. The conditions included center-light, with glasses, happy, left-light, without
glasses, normal, right-light, sad, sleepy, surprised and wink. These different variations
provided by the database is able to ensure the system to be operated consistently in variety of
situations and conditions.
Limitations of the image
The input image for the proposed approach has to be frontal, upright and only a single face.
Although the system is designed to be able to recognize the student with glasses and without
glasses, student should provide both facial images with and without glasses to be trained to
increase the accuracy to be recognized without glasses. The training image and testing image
should be captured by using the same device to avoid quality difference. The students have to
register in order to be recognized. The enrolment can be done on the spot through the user-
friendly interface.
These conditions have to be satisfied to ensure that the proposed approach can perform well.
Face Detection: Face detection is a process of locating a face inside an image frame,
regardless of the identity of that face. Before recognizing a face, it is first essential to detect
and extract the faces form the original pictures. Face Detection target on finding the faces
(area and size) in an image and probably extract them to be used by the face recognition
algorithm. In recent years, many methods are proposedfordetecting the face.
In face detection methods, those who are depending on training sets to capture the huge
unevenness in facial features have enticed much attention and given the best results.
Generally, these methods scan the input picture at all potential area and scales then as the
sub windows either as non-face or face. Viola and Jones presented an effective detection
technique using Haar-like features and AdaBoost as a quick training algorithm. For
recognizing a face, the algorithms compare only faces. Any other element in the picture that
is not part of a face deteriorates the recognition.
There are several existing algorithms for detecting faces. Prior to year 2000 there were many
techniques for face detection, however they were mostly unreliable, slow and require manual
inputs. In 2001 Viola and Jones invented the Haar-based-cascade a Classifier that

revolutionize the face detection method. It can detect objects in real time with an accuracy of
95%. It works not only for frontal face view but it can detect faces from side view as well.
Face Preprocessing:
Testing set and training set images are captured using a camera. There are unwanted noise and
uneven lighting exists in the images. Therefore, several pre-processing steps are necessary
before proceeding to feature extraction.
Pre-processing steps that would be carried out include scaling of image, median filtering,
conversion of colour images to grayscale images and adaptive histogram equalization.
Any of the previous methods can be used for extracting faces from input pictures. The next
step is to pre-process these faces in order to make the training phase easier and improve the
probability to recognize a person correctly. The training data will be standardized. Not all the
pictures have the same zoom on the face and have maybe not all the same size. Most of the
algorithms for facial recognition require the same size for the entire training set. Pre-
processing includes different modifications. First of all, the faces need to be centered in the
picture in the same way. The location of the two eyes and the nose is often used as a landmark
for centering faces. The aim is to have the eyes at the same level and the nose at the same
position for all images. To apply these modifications, the coordinates of the landmarks are
needed. For that, it is possible to use a Haar-cascade classifier for detecting nose and eyes.
After detecting a face in the frame, we can now process the face inside the green rectangle.
Face recognition is susceptible to changes in lighting conditions, face orientation, face
expression, so it is paramount to diminish these differences as much as possible. There are
numerous techniques to eliminate those issues. Some of these techniques are:-
 Geometrical transformation and cropping: This procedure includes resizing of the
image and rotating the image as well as removing background.
 Histogram equalization: This process standardizes the brightness and contrast of the
image.
 Smoothing: In this process, the image noise is eliminated by applying some filters.
 Elliptical mask: The elliptical mask removes some remaining background.
Processing images can be computationally expensive in a PC or laptop, similarly in mobile
devices. It requires higher processing power. Therefore, minimalizing the image processing
in the mobile device is a must to achieve a real-time face recognition system
Database development:
In this Biometric based system collection of every individual is required. This database
development phase consists of image capture of every individual student and extracting the
Bio-metric feature for every individual, in our proposed system it is face, and after it is
enhanced using pre-processing techniques and to be stored in the database.

Face Recognition/ Post Processing:
Here the detected cropped faces are compared with the trained images from the database
using correlation. if any of the cropped image is recognized then that Id would be marked
present in the attendance data sheet.
Proposed Algorithm
1. Capture the student’s image through camera.
2. Detect each and every individual face by apply face detection algorithm.
3. Extract the ROI (Region of Interest) in rectangular bounding box.
4. Converting to gray scale, apply histogram equalization and resize to 100x 100 i.e., apply
pre-processing.
5. If image captured then
Store in database
Else
Apply LBPH (for feature extraction)
Apply SVM (for classification)
End if
6.Post-processing

Component diagram
Component diagram:
Fig 3: component diagram of face recognition for e-attendance
Entity Relationship diagram
Fig: Relationship between user and face detection

Dataflow diagram
Fig: context diagram
Fig: data flow diagram (level 1)

Fig: data flow diagram (Face Recognition)
Facility required for purposed work
The previous approach in which manually taking 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.
This method could easily allow forimpersonation and the attendance sheet could be
stolen or lost.
Biometric-basedtechniques have emerged as the most promising option for recognizing
individuals in recentyears since, instead of authenticating people and granting them access to
physical and virtualdomains based on passwords,PINs, smart cards, plastic cards, tokens,
keys and so forth, these methods examine an individual’s physiological and/or behavioral
characteristics in order todetermine and/or ascertain his identity.
Biometric based technologies include identification based on physiological characteristics
(such as face, fingerprints, finger geometry, hand geometry, hand veins, palm, iris, retina, ear

and voice) and behavioral traits (such as gait, signature and keystroke dynamics). Face
recognition appears to offer several advantages over other biometric methods, a few of which
are outlined here: Almost all these technologies require some voluntary action by the user, i.e.,
the user needs to place his hand on a hand-rest for fingerprinting or hand geometry detection
and has to stand in a fixed position in front of a camera for iris or retina identification.
However, face recognition can be done passively without any explicit action or participation
on the part of the user since face images can be acquired from a distance by a camera. This is
particularly beneficial for security and surveillance purposes. Furthermore, data acquisition in
general is fraught with problems for other biometrics: techniques that rely on hands and fingers
can be rendered useless if the epidermis tissue is damaged in some way (i.e., bruised or
cracked).
Tools and Environments Used
SystemRequirements Specification
 Technical Requirement
Hardware Requirements
 A standalone computer (intel Processor, 4gb ram or higher)
 High-quality wireless camera to capture images
 Secondarymemory to store all the images and database
Software requirements
 Windows operating System (7, 8, 10)
 Java development kit
 Java applets
 Latest version of all libraries
 Functional Requirements
System functional requirement describes activities and services that must provide.
 A user must be able to manage student records.
 An only authorized user must be able to use the system.
 A system must be attached to wireless camera and face recognition should be
smooth. The administrator or the person who will be given the access to
the systemmust
 login into the system before using it. The information must be entered and
managed properly.

 Non-Functional Requirements
Non-functional Requirements are characteristics or attributes of the system that can
judge its operation. The following points clarify them:
 Accuracy and Precision: the system should perform its process with accuracy
and precision to avoid problems.
 Flexibility: the system should be easy to modify, any wrong should be correct.
 Security: the system should be secure and saving student's privacy.
 Usability: the systemshould be easy to deal with and simple to understand.
 Maintainability: the maintenance group should be able to cope up with any
problem when occurs suddenly.
 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 shown 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 extended 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.
 Student Requirements
 A student needs to enter the properdetails while registering him/her.
 He/ She needs to sit properly and capture 10-15 images of himself/herself in
different directions and expressions.
 At the time of taking attendance, students need to sit properly facing the
camera.
 Teaching Staff Requirements
 The faculty needs to log into the system at the time of attendance.
 The faculty needs to enter lecture details before starting the attendance
process.
 If the entered lecture details don’t match with the ones in the database (excel
sheet) an error dialog will be displayed.
 Administrator Requirements
 The administrator needs to log into the system at the time of registering the
students in 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.

Conclusion
In this approach, a face recognition based automated student attendance system is thoroughly
described. Theproposed approachprovides a method to identify the individuals bycomparing
their input image obtained from recording video frame with respect to train image..
Extraction of features from the facial image is performed by applying both LBP and PCA.
The algorithm designed to combine LBP and PCA able to stabilize the system by giving
consistent results. The accuracy of this proposed approach is 100 % for high-quality images,
92.31 % for low-quality images and 95.76 % of Yale face database when two images per
person are trained.
As a conclusion for analysis, the extraction of facial feature could be challenging especially
in different lighting. In pre-processing stage, Contrast Limited Adaptive Histogram
Equalization (CLAHE) able to reduce the illumination effect. CLAHE perform better
compared to histogram equalization in terms of contrast improvement. Enhanced LBP with
larger radius size specifically, radius size two, perform better compared to original LBP
operator, with less affected by illumination and more consistent compared to other radius
sizes.
It saves time and effort, especially if itis a lecture with huge number of students. Automated
Attendance System has been envisioned for the purpose of reducing the drawbacks in the
traditional (manual) system.
Future Scope
It can be easily implemented at any institute or organization. A method could be proposed to
illustrate robustness against the variations that is, in near future we could build a system which
would be robust and would work in undesirable conditions too. Here it is proposed for an
institute to take the attendance of the students but in future it can be used to do the same work
at entry as well as exit points. I am working to improve the face recognition effectiveness to
build more efficient systems in near future. In further work, authors intend to improve face
recognition effectiveness by using the interaction among our system, the users and the
administrators. On the other hand, our system can be used in a completely new dimension of
face recognition application, mobilebased face recognition, which can be an aid for common
people to know about any person being photographed by cell phone camera including proper
authorization for accessing a centralized database.

References
Robinson-Riegler, G., & Robinson-Riegler, B. (2008). Cognitive psychology: applying the
science
of the mind. Boston, Pearson/Allyn and Bacon.
Margaret Rouse. (2012). What is facial recognition? - Definition from WhatIs.com.
Available at:
http://whatis.techtarget.com/definition/facial-recognition.
Solon, O. (2017). Facial recognition database used by FBI is out of control, House
committee hears. the Guardian. Available at:
https://www.theguardian.com/technology/2017/mar/27/us-facial-recognition-database-fbi-
drivers-licenses-passports.
RobertSilk. (2017). Biometrics: Facialrecognition tech coming to an airport near you:
Travel Weekly. Available at:
http://www.travelweekly.com/Travel-News/Airline-News/Biometrics-Facial-recognition-
tech-coming-airport-near-you.
Sidney Fussell. (2018). NEWS Facebook's New Face Recognition Features: What We Do
(and Don't) Know. Available at:
https://gizmodo.com/facebooks-new-face-recognition-features-what-we-do-an-1823359911 .
deAgonia, M. (2017). Apple's Face ID [The iPhone X's facial recognition tech explained].
Computerworld. Available at:
https://www.computerworld.com/article/3235140/apple-ios/apples-face-id-the-iphone-xs-
facial-recognition-tech-explained.html.
Ashley DuVal. (2012). Face Recognition Software -History of Forensic Psychology. [online]
Available at:
http://forensicpsych.umwblogs.org/research/criminal-justice/face-recognition-software.
Jesse Davis West. (2017). History of Face Recognition - Facialrecognition software.
[online] Available at:
https://www.facefirst.com/blog/brief-history-of-face-recognition-software.
Reichert, C. (2017). Intel demos 5G facial-recognition payment technology | ZDNet. [online]
ZDNet. Available at:
http://www.zdnet.com/article/intel-demos-5g-facial-recognition-payment-technology.
Handbookon face recognition (2011) Editors: Li, Stan Z., Jain, Anil K. (Eds.).

project synopsis face recognition attendance system

project synopsis face recognition attendance system

More Related Content

What's hot (20)

Similar to project synopsis face recognition attendance system (20)

Recently uploaded (20)

project synopsis face recognition attendance system