Description
ABSTRACT
Authentication is one of the significant issues in the era of information system. Among other things, human face recognition (HFR) is one of known techniques which can be used for user authentication. As an important branch of biometric verification, HFR has been widely used in many applications, such as video monitoring/surveillance system, human-computer interaction, door access control system and network security. This paper proposes a method for student attendance system in classroom using face recognition technique.
TABLE OF CONTENTS
COVER PAGE
TITLE PAGE
APPROVAL PAGE
DEDICATION
ACKNOWELDGEMENT
ABSTRACT
CHAPTER ONE
- INTRODUCTION
- BACKGROUND OF THE PROJECT
- AIM OF THE PROJECT
- OBJECTIVE SCOPE OF THE PROJECT
- PURPOSE OF THE PROJECT
- PROBLEM STATEMENT
- SCOPE OF THE PROJECT
- SIGNIFICANCE OF THE PROJECT
- PROJECT JUSTIFICATION
- APPLICATION OF THE PROJECT
- LIMITATION OF THE PROJECT
- PROJECT ORGANISATION
CHAPTER TWO
LITERATURE REVIEW
2.1 OVERVIEW OF ATTENDANCE MANAGEMENT
2.2 OVERVIEW OF THE STUDY
2.3 REVIEW OF RELATED STUDIES
CHAPTER THREE
METHODOLOGY
- PROPOSED SYSTEM
- SYSTEM ARCHITECTURE
- SYSTEM MODULES AND THEIR FUNCTIONS
- PROPOSED ALGORITHM
- FLOW CHART
- SOFTWARE DESCRIPTION
CHAPTER FOUR
4.0 RESULT AND DISCUSSION
CHAPTER FIVE
- CONCLUSIONS
- RECOMMENDATION AND FUTURE SCOPE
- REFERENCES
CHAPTER ONE
1.0 INTRODUCTION
Maintaining attendance is very important in all learning institutes for checking the performance of students. In most learning institutions, student attendances are manually taken by the use of attendance sheets issued by the department heads as part of regulation. The students sign in these sheets which are then filled or manually logged in to a computer for future analysis. This method is tedious, time consuming and inaccurate as some students often sign for their absent colleagues. This method also makes it difficult to track the attendance of individual students in a large classroom environment [1]. In this project, we propose the design and use of a face detection and recognition system to automatically detect students attending a lecture in a classroom and mark their attendance by recognizing their faces.
While other biometric methods of identification (such as iris scans or fingerprints) can be more accurate, students usually have to queue for long at the time they enter the classroom [2]. Face recognition is chosen owing to its non-intrusive nature and familiarity as people primarily recognize other people based on their facial features [3]. This (facial) biometric system will consist of an enrollment process in which the unique features of a persons’ face will be stored in a database and then the processes of identification and verification. In these, the detected face in an image (obtained from the camera) will be compared with the previously stored faces captured at the time of enrollment. Since all parts of the image have to be considered, the brightness-based approach takes longer time to process the image and is also more complicated. To make the process short and simple, the image has to be transformed into a certain model.
1.2 BACKGROUND OF THE STUDY
In traditional face-to-face (F2F) class setting, student attendance record is one of the important issues dealt with any school, college and university from time to time. To keep the student attendance record valid and correct, the faculty staff should have a proper mechanism for verifying and maintaining or managing that attendance record on regular basis. In general, there are two types of student attendance system, i.e. manual attendance system (MAS) and automated attendance system (AAS). By practicing manual recording, faculty staff may experience difficulty in both verifying and maintaining each student’s record in classroom environment on regular basis, especially in classes attended by a large number of students. In practice, the manual system also requires more time for recording and calculating the average attendance of each enrolled student. On the other hand, the automated attendance system may offer some benefits to to the faculty, at least it may lessen the administrative burden of its staff. Particularly, for attendance system which adopts human face recognition (HFR) technique, such a system commonly involves the process of extracting key features from any facial image of student captured at the time he/she is entering the classroom, or when everybody already occupies his/her seat in the classroom. Upon its successful recognition, it proceeds to marking that recognized student’s attendance automatically. Following that general idea, the discussion of this paper is based on the known face recognition techniques in its endeavor to develop a specific computer application which can be used for recognizing any enrolled student automatically from the digital images captured in classroom.
In general, there are two known approaches to HFR, i.e. feature-based and brightness-based approach. The feature- based approach uses key point features of the face, such as edges, eyes, nose, mouth, or other special characteristics. Therefore, the calculation process only covers some parts of the given image that have been extracted previously. On the other hand, the brightness-based approach calculates all parts of the given image. It is also known as holistic-based or image-based approach.
1.2 AIM OF THE PROJECT
The aim is to automate and make a system that is useful to the organization such as an institute. The efficient and accurate method of attendance in the school environment that can replace the old manual methods. This method is secure enough, reliable and available for use.
1.3 OBJECTIVES OF THE PROJECT
The overall objective is to develop an automated class attendance management system comprising of a desktop application working in conjunction with a mobile application to perform the following tasks:
- To detect faces real
- To recognize the detected faces by the use of a suitable
- To update the class attendance register after a successful
- To design an architecture that constitutes the various components working
1.4 PURPOSE OF THE PROJECT
Traditional method for taking attendance is Roll Number of student and record the attendance in sheet which takes a lot of time. Because of this, face recognition is used to make it fast and secured.
1.5 SCOPE OF THE PROJECT
We are setting up to design a system comprising of two modules. The first module (face detector) is a mobile component, which is basically a camera application that captures student faces and stores them in a file using computer vision face detection algorithms and face extraction techniques. The second module is a desktop application that does face recognition of the captured images (faces) in the file, marks the students register and then stores the results in a database for future analysis.
1.6 PROBLEM DEFINITION
The traditional manual methods of monitoring student attendance in lectures are tedious as the signed attendance sheets have to be manually logged in to a computer system for analysis. This is tedious, time consuming and prone to inaccuracies as some students in the department often sign for their absent colleagues, rendering this method ineffective in tracking the students’ class attendance. Use of the face detection and recognition system in lieu of the traditional methods will provide a fast and effective method of capturing student attendance accurately while offering a secure, stable and robust storage of the system records, where upon authorization; one can access them for purposes like administration, parents or even the students themselves[4].
1.7 SIGNIFICANCE OF THE PROJECT
This system offers its clients with simple, efficiently manageable software that provides a platform to maintain attendance records. An student’s identity and attendance is instantly proved just by looking at the sensor. The importance of this system is as below:
- Removes the risk of Manual Errors
Facial recognition software gives institution a means of tracing their students’ attendance, by further removing human mistakes. This means to keep track of the precise quantity of hours students are in, this is because in school pattern of taking attendance – student will sign in when he or she resume and also sign out after dismissal.
- Automated plus Accurate
Face recognition precisely reports all the features of attendance, absenteeism, and also over time. The identification procedure is spot-on every single time at a speed that is now practically possible. This scheme can match thousands of operators in less than a second, plus the software offers info that is 100% precise without you lifting a finger.
- Saves Time
There is no requisite for employees to touch the surface of the scheme to clock in plus out. Facial recognition permits students to sign in and out inside seconds, instantly removing the inconvenience of swiping cards otherwise signaling badges around. This saves time and effort, resulting in a satisfactory schooling environment.
- Upsurges Security
First off, this technology overall avoids the subjects of early and late striking, in addition to “friend punching.”
- Easily Installable
Facial recognition systems are very easy to install. You can easily integrate one of the leading benefits of Integrated Biometric facial schemes. These are easy towards programming into your firm’s PC system. Most of the systems would work with the software that you have by now installed.
1.8 PROJECT JUSTIFICATION
This project serves to automate the prevalent traditional tedious and time wasting methods of marking student attendance in classrooms. The use of automatic attendance through face detection and recognition will increase the effectiveness of attendance monitoring and management.
This method could also be extended for use in examination halls to curb cases of impersonation as the system will be able to single out the imposters who won’t have been captured during the enrollment process. Applications of face recognition are widely spreading in areas such as criminal identification, security systems, image and film processing [5]. The system could also find applications in all authorized access facilities.
1.9 APPLICATION OF THE PROJECT
Apart from using this device for students’ attendance, this device can also be used for places like:
- Organisation
- Religious centres
- Offices, and any other meeting places, for automatically taking attendance of the people present.
1.10 PROBLEM AND LIMITATION OF THE PROJECT
- It can only detect face from a limited distance.
- It cannot repeat live video to recognize missed faces.
1.6 PROJECT WORK ORGANISATION
The various stages involved in the development of this project have been properly put into five chapters to enhance comprehensive and concise reading. In this project thesis, the project is organized sequentially as follows:
Chapter one of this work is on the introduction to this study. In this chapter, the background, aim, significance, objective, purpose, applications, problem identification, limitation and problem of this work was discussed.
Chapter two is on literature review of the study. In this chapter, all the literature pertaining to this work was reviewed.
Chapter three is on design methodology. In this chapter all the method involved in this study were discussed.
Chapter four is on test and result analysis. All testing that result accurate functionality was analyzed.
Chapter five is on conclusion, recommendation and references.
CHAPTER FIVE
5.1 CONCLUSION
This system aims to build an effective class attendance system using face recognition techniques. The proposed system will be able to mark the attendance via face Id. 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.
In this Face Recognition based attendance system, a new deep learning based face recognition attendance system is proposed. The entire procedure of developing a face recognition component by combining state of-the-art methods and advances in deep learning is described. It is determined that with the smaller number of face images along with the proposed method of augmentation high accuracy can be achieved.
These results are enabling further research for the purpose of obtaining even higher accuracy on smaller datasets, which is crucial for making this solution production-ready. The future work could involve exploring new augmentation processes and exploiting newly gathered images in runtime for automatic retraining of the embedded data. Developing a specialized classifying solution for this task could potentially lead to achieving higher accuracy on a smaller dataset. This deep learning based solution does not depend on GPU in runtime. Thus, it could be applicable in many other systems as a main or a side component that could run on a cheaper and low-capacity hardware, even as a general-purpose Internet of things (IoT) device.
5.2 RECOMMENDATION AND FUTURE STUDY
In this project, it is observed that only faces are detected only when the person face is clearly visible and when the face of a person cannot be detected when the person is standing in any other direction so for this further study is required for detecting the faces of the person clearly in all directions by using further deep learning algorithms.
Reviews
There are no reviews yet.