This document provides a project synopsis for a face recognition-based e-attendance system. It discusses developing an automated attendance system using face recognition technology to address issues with traditional manual attendance methods, such as being time-consuming and allowing for fraudulent attendance. The objectives are to help teachers track and manage student attendance and absenteeism more efficiently. The proposed system uses face detection and recognition algorithms to automatically mark student attendance based on detecting faces in the classroom. It includes modules for image capture, face detection, preprocessing, database development, and postprocessing for recognition. Feasibility analysis indicates the technical feasibility of the system using existing technologies. Methodology diagrams show the training and recognition workflows that involve face detection, feature extraction, and classification.
This documentation provides a brief insight of face recognition based attendance system using neural networks in terms of product architecture which can be used for educational purpose.
Attendance Management System using Face RecognitionNanditaDutta4
The project ppt presentation is made for the academic session for the completion of the work from Bharati Vidyapeeth Deemed University(IMED) MCA department
Automatic Attendance system using Facial RecognitionNikyaa7
It is a boimetric based App,which is gradually evolving in the universal boimetric solution with a virtually zero effort from the user end when compared with other boimetric options.
Face Recognition Based Attendance System using Machine LearningYogeshIJTSRD
In the era of modern technologies emerging at rapid pace there is no reason why a crucial event in education sector such as attendance should be done in the old boring traditional way. Attendance monitoring system will save a lot of time and energy for the both parties teaching staff as well as the students. Attendance will be monitored by the face recognition algorithm by recognizing only the face of the students from the rest of the objects and then marking the students as present. The system will be pre feed with the images of all the students enrolled in the class and with the help of this pre feed data the algorithm will detect the students who are present and match the features with the already saved images of the students in the database. Benazir Begum A | Sreeyuktha R | Haritha M P | Vishnuprasad "Face Recognition Based Attendance System using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39856.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/39856/face-recognition-based-attendance-system-using-machine-learning/benazir-begum-a
This documentation provides a brief insight of face recognition based attendance system using neural networks in terms of product architecture which can be used for educational purpose.
Attendance Management System using Face RecognitionNanditaDutta4
The project ppt presentation is made for the academic session for the completion of the work from Bharati Vidyapeeth Deemed University(IMED) MCA department
Automatic Attendance system using Facial RecognitionNikyaa7
It is a boimetric based App,which is gradually evolving in the universal boimetric solution with a virtually zero effort from the user end when compared with other boimetric options.
Face Recognition Based Attendance System using Machine LearningYogeshIJTSRD
In the era of modern technologies emerging at rapid pace there is no reason why a crucial event in education sector such as attendance should be done in the old boring traditional way. Attendance monitoring system will save a lot of time and energy for the both parties teaching staff as well as the students. Attendance will be monitored by the face recognition algorithm by recognizing only the face of the students from the rest of the objects and then marking the students as present. The system will be pre feed with the images of all the students enrolled in the class and with the help of this pre feed data the algorithm will detect the students who are present and match the features with the already saved images of the students in the database. Benazir Begum A | Sreeyuktha R | Haritha M P | Vishnuprasad "Face Recognition Based Attendance System using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39856.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/39856/face-recognition-based-attendance-system-using-machine-learning/benazir-begum-a
"Attendance Management System bridges the effective communication between students, teachers, and parents by keep them notified about their wards' attendance via Email or SMS.
A.T.S.I. offers best biometric attendance management system, face recognition attendance system, fingerprint based attendance system, and RFID based attendance system and gives flexibility to institutions to choose the suitable system for them."
Face Detection and Recognition System (FDRS) is a physical characteristics recognition technology, using the inherent physiological features of humans for ID recognition. The technology does not need to be carried about and will not be lost, so it is convenient and safe for use
Face recognition attendance system using Local Binary Pattern (LBP)journalBEEI
Attendance is important for university students. However, generic way of taking attendance in universities may include various problems. Hence, a face recognition system for attendance taking is one way to combat the problem. This paper will present an automated system that will automatically saves student’s attendance into the database using face recognition method. The paper will elaborate on student attendance system, image processing, face detection and face recognition. The face detection part will be done by using viola-jones algorithm method while the face recognition part will be carried on by using local binary pattern (LBP) method. The system will ensure that the attendance taking process will be faster and more accurate.
Face recognition system plays an important role when its comes to security, In this slide using of neural networking system for face recognition system has demonstrated.
The slide was prepared on the purpose of presentation of our project face detection highlighting the basics of theory used and project details like goal, approach. Hope it's helpful.
"Attendance Management System bridges the effective communication between students, teachers, and parents by keep them notified about their wards' attendance via Email or SMS.
A.T.S.I. offers best biometric attendance management system, face recognition attendance system, fingerprint based attendance system, and RFID based attendance system and gives flexibility to institutions to choose the suitable system for them."
Face Detection and Recognition System (FDRS) is a physical characteristics recognition technology, using the inherent physiological features of humans for ID recognition. The technology does not need to be carried about and will not be lost, so it is convenient and safe for use
Face recognition attendance system using Local Binary Pattern (LBP)journalBEEI
Attendance is important for university students. However, generic way of taking attendance in universities may include various problems. Hence, a face recognition system for attendance taking is one way to combat the problem. This paper will present an automated system that will automatically saves student’s attendance into the database using face recognition method. The paper will elaborate on student attendance system, image processing, face detection and face recognition. The face detection part will be done by using viola-jones algorithm method while the face recognition part will be carried on by using local binary pattern (LBP) method. The system will ensure that the attendance taking process will be faster and more accurate.
Face recognition system plays an important role when its comes to security, In this slide using of neural networking system for face recognition system has demonstrated.
The slide was prepared on the purpose of presentation of our project face detection highlighting the basics of theory used and project details like goal, approach. Hope it's helpful.
The study is an online, computer aided tool that was designed primarily for the conduct of online examination. The system
was created using PHP, a web based scripting language, and MySQ
L as the database software. The system focuses on
the automation of students' examinations; preparation, scheduling, checking and grading. A database is provided for the
storage of exam questions, answers to questions and students' records. The system allo
ws instructors to create an exam
by entering questions with its corresponding answers into the database. Instructors are provided with three options on the
type of exam; these include, True or False, Multiple Choice and Fill in the Blanks.
There are three
account types based on the intended users. One is the Administrator Account; this can be used to create
instructor accounts. It can also be used to delete or suspend other accounts based on activity status. The Instructor
Account allows teachers to create
student accounts and enroll the same. This account can be used also to create,
activate, edit, delete exams and monitor students' performances. The Student Account is for the officially enrolled students
where they can take exams and view scores even from
previous examinations.
This software allows instructors to keep track of students' performances from all exams since the results will be stored in a
database linked to an online system. While taking the online exam, students can choose the number of exa
m questions
that will be displayed on the screen at a given time.
A student can take the exam only on the specified date and time set by the instructor. Ideally, a particular exam should be
taken only once. In cases of retakes due to valid reasons and spe
cial exam considerations, the instructor is given the
option to administer the previously activated exam, edit or create a new set of questions.
One limitation though, this online system is not to be used to compute for the class performance for the final
grade since
this requires other components such as seat works, graded recitations, laboratory activities, etc. This only computes and
shows the scores from previous exams and the average.
That is presentation based on hyper text markup language
( Html) basically hear we defined html in deep manner
hear i defined each topic like html, body ,title, head, and other tags
na dhear i'm also defined the types f tags and so on
so enjoy and also defined that structure design
please explore and tag me
Machine learning ppt
college presentation on Machine Learning Programming releated them. explain each and every Point in detail so. thats why they are easily to explain in the
Seminar topic on holography, they are used for final year student or 3rd year student to get selection of topic on seminar and explain in front of collage students
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Planning Of Procurement o different goods and services
Project synopsis on face recognition in e attendance
1. PROJECT SYNOPSIS OF
MAJOR PROJECT
ON
“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: 2020-21
Submitted To
DR. ABDUL KALAM TECHNICAL UNIVERSITY
UTTAR PRADESH
Submitted By
NITESH KUMAR DUBEY
1836210903
Under the guidance of
Prof.: Mr. Vipin Jaiswal
HOD: Mrs. Arifa Khan
DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING
Lucknow Institute of Technology
Lucknow
2. Table of Contents
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.References
3. 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 personis stored in the database and after that, there are some processes of
identification and verification of the person. These two processes compare the biometric
feature of a person with previously stored template captured at the time of enrolment of a
student. Biometric templates can be of many types like Fingerprints, Eye Iris, voice etc. Our
system uses the face recognition approachfor the automatic attendance of the students in the
classroom environment without student intervention. 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.
Usually, a roll-call is taken to determine whether the student is present in the class or not,
which usually wastes a lot oftime. In recent years, with the emerging technology and with the
development of deep learning, face recognition has made great achievements, which leads us
to a new way of thinking to solve the problem of student’s enrollment. So, in order to save
time, the idea to count the number of students in a class automatically based on face
recognition is incorporated. This system is developed by using face recognition technique
which is used to detect the face of an individual. There are many different face recognition
algorithms introduced to increase the efficiency of the system. The system provides an
increased accuracy due to the use of a large number of features like Shape, color, LBP,
4. 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. This existing system requires correctfeed oninput
into the respective field. Therefore, we are in a need of an automated system for marking and
maintaining attendance of the students. Let us supposethat the wrong inputs are entered, the
application resist to work. So, the user finds it difficult to use the existing system.
Traditional student attendance marking technique is often facing a lot of trouble. The face
recognition student attendance system emphasizes its simplicity by eliminating classical
student attendance marking technique such as calling student names or checking respective
identification cards. There are not only disturbing the teaching process but also causes
distraction for students during exam sessions. Apart from calling names, attendance sheet is
passed around the classroomduring the lecture sessions. The lecture class especially the class
with a large number ofstudents might find it difficult to have the attendance sheetbeing passed
around the class. Thus, face recognition student attendance system is proposed in order to
replace the manual signing of the presence of students which are burdensome and causes
students get distracted in order to sign for their attendance. Furthermore, the face recognition
based automated student attendance system able to overcome the problem of fraudulent
approachand lecturers does not have to count the number of students several times to ensure
the presence of the students.
The paper proposed byZhao, 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.
5. 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 ofmobile 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.
Avoiding the time losses during class started.
6. Feasibility study
A feasibility study is a high-level capsule version of the entire System analysis and Design
Process. Thestudy 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 system can 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.
Technicalfeasibility:
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
Financialand Economic Feasibility:
7. The system developed 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 bytesting images (recognize the unknown input image) shown in Figure 1 and Figure
2 respectively.
Fig 1: Flow of proposed Approach (training part)
8. 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
9. Database development
Postprocessing
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 perindividual; each image ofthe 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 oflocating 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
10. 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 personcorrectly. 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.
11. Face Recognition/PostProcessing:
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.
ProposedAlgorithm
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
13. Fig 3: component diagram of face recognition for e-attendance
Entity Relationship diagram
Fig: Relationship between user and face detection
Dataflow diagram
15. 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.
The ability to computethe attendance percentage becomes amajor taskas 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. An automatic attendance
management system using biometrics would provide the needed solution. The results showed
improved performance over manual attendance management system. Biometric-based
techniques have emerged as the most promising option for recognizing individuals in recent
years since, instead of authenticating people and granting them access to physical and virtual
domains based on passwords,PINs, smart cards, plastic cards, tokens, keys and so forth, these
16. methods examine an individual’s physiological and/or behavioral characteristics in order to
determine 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 somevoluntary action bythe 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 forother biometrics: techniques that rely onhands 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
TechnicalRequirement
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.
17. 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 personwho will be given the access to the system
must
login into the system before using it.
The information must be entered and managed properly.
Non-FunctionalRequirements
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 system should be easy to deal with and simple to understand.
Maintainability: the maintenance group should be able to copeup 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 StaffRequirements
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’tmatch with the ones in the database (excel
sheet) an error dialog will be displayed.
18. As the students are recognized by the system, the attendance report will be
generated and shown to the faculty.
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 amethod to identify the individuals bycomparing
their input image obtained from recording video frame with respect to train image. This
proposed approach able to detect and localize face from an input facial image, which is
obtained from the recording video frame. Besides, it provides a method in pre-processingstage
to enhance the image contrast and reduce the illumination effect. 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 proposedapproachis 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
19. 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.
In order to maintain the attendance this system has been proposed. It replaces the manual
system with an automated system which is fast, efficient, costand time saving as replaces
the stationary material and the paper work. Hence this system is expected to give desired
results and in future could be implemented for logout. Also, the efficiency could be
improved by integrating other techniques with it in near future. In this system we have
implemented an attendance records to assist Faculty. It saves time and effort, especially if it
is a lecture with huge number of students. Automated Attendance System has been
envisioned for the purposeof 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 proposedforan 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, mobile
based face recognition, which can be an aid for common people to know about any person
being photographed by cell phone camera including properauthorization for accessing a
centralized database.
20. References
Robinson-Riegler, G., & Robinson-Riegler, B. (2008). Cognitive psychology: applying the
science
of the mind. Boston, Pearson/Allyn and Bacon.
Margaret Rouse. (2012). Whatis facial recognition? - Definition from WhatIs.com.
Available at:
http://whatis.techtarget.com/definition/facial-recognition.
Solon, O. (2017). Facialrecognition databaseused 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: WhatWe 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 iPhoneX'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.
21. Handbookon face recognition (2011) Editors: Li, Stan Z., Jain, Anil K. (Eds.).
Available at: https://www.springer.com/gp/book/9780857299314.