STUDENTS ATTENDANCE MAINTENANCE MANAGEMENT SYSTEM USING FACE RECOGNITION AND OPEN SOURCE LIBRARY
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Authentication is one of the significant issues in the era of information system. Among other things, human face recognition (HFR) and open source library are known as techniques which can be used for user authentication in attendance management system and these techniques eliminate the need for manual attendance-taking, which is time-consuming and prone to errors. As an important branch of biometric verification, human face recognition (HFR) and open source library have 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.
CHAPTER ONE
1.0 INTRODUCTION
1.2 BACKGROUND OF THE STUDY
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 (Lim et al., 2019).
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 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.
In this project, we propose the design and use of a face recognition system with open source library of Python 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 (Savvides et al., 2010). Face recognition with open source library of Python is chosen owing to its non-intrusive nature and familiarity as people primarily recognize other people based on their facial features (Marko et al., 2010). This system involves creating a dataset of your face and train the system with that dataset, with this trained model we implemented attendance system to recognize the face and mark the attendance of user using provided user id. 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 STATEMENT OF THE PROBLEM
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 recognition system and open source library 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 (Nazare, 2016).
This system covers an attendance system using Face Recognition feature with open source library of Python. You can create a dataset of your face and train the system with that dataset, with this trained model we implemented attendance system to recognize the face and mark the attendance of user using provided user id.
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 recognition and open source library of Python 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 and open source library of Python are widely spreading in areas such as criminal identification, security systems, image and film processing (Rekha et al., 2017). The system could also find applications in all authorized access facilities.
1.3 AIM AND OBJECTIVES OF THE PROJECT
The aim is to automate and make a system that is useful to the organization such as an institute which will serve as a means of providing an efficient and accurate method of attendance in the school environment that can replace the old manual methods.
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 time.
- To recognize the detected faces by the use of a suitable algorithm.
- To detect faces within the image and compares it with the listed faces in the database
- To update the class attendance register after a successful match.
- To design architecture that constitutes the various components working harmoniously.
REVIEW OF RELATED STUDIES
ShubhobrataBhattacharya et al (2018) proposed a model of an automated attendance system. The model focuses on how face recognition incorporated with Radio Frequency Identification (RFID) detect the authorized students and counts as they get in and get out form the classroom. The system keeps the authentic record of every registered student. The system also keeps the data of every student registered for a particular course in the attendance log and provides necessary information according to the need.
Omar et al (2018) have designed and implemented an attendance system which uses iris biometrics. Initially, the attendees were asked to register their details along with their unique iris template. At the time of attendance, the system automatically took class attendance by capturing the eye image of each attendee, recognizing their iris, and searching for a match in the created database. The prototype was web based.
Rekha et al., (2017) proposed an attendance system based on facial recognition. The algorithms like Viola-Jones and Histogram of Oriented Gradients (HOG) features along with Support Vector Machine (SVM) classifier were used to implement the system. Various real time scenarios such as scaling, illumination, occlusions and pose was considered by the authors. Quantitative analysis was done on the basis of Peak Signal to Noise Ratio (PSNR) values and was implemented in MATLAB GUI.
Shu et al. (2010) researches to get best facial recognition algorithm (Eigenface and Fisherface) provided by the Open CV 2.4.8 by comparing the Receiver Operating Characteristics (ROC) curve and then implemented it in the attendance system. Based on the experiments carried out in this paper, the ROC curve proved that, Eigenface achieves better result than Fisherface. System implemented using Eigenface algorithm achieved an accuracy rate of 70% to 90%.
Arsenovic et al (2017) proposed a method for student attendance system in classroom using face recognition technique by combining Discrete Wavelet Transforms (DWT) and Discrete Cosine Transform (DCT). These algorithms were used to extract the features of student’s face followed by applying Radial Basis Function (RBF) for classifying the facial objects. This system achieved an accuracy rate of 82%.
Methodology
To achieve the said aim, System is developed as a client-server based Cloud application. The system is designed to transfer heavy weighed task like face detection and recognition from client local system to cloud server, since image processing might be heavy weighed task, especially when amount of data is vast and large. The task of the said system is to capture the face of every student and to store it within the database for their attendance.
The main task of the client local system is to listen for attendance request and continuous capturing of images from the Raspberry PI connected cameras mounted in front of the classrooms.
The system uses Raspberry PI connected Cameras affixed in front of a classroom disregarding of their location to continuously capture image of the entire class at fixed interval, over the duration of a lecture and sends images through the intranet to the cloud server for processing. The server processes the images by detecting and identifying the human faces contained, extracts the faces and matches them with the registered faces of the students stored on the database. During the process of face identification, only the student that is registered with the course is marked as present, rest is kept unprocessed and admin (teacher) is notified about the unrecognized faces. On correct identification of a student face, the attendance register for the course is flagged as present for the student, otherwise it is marked as an absent.
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