Open AccessSurvey on Face Recognition Based Attendance Management System Using HOG Feature Extraction and SVM Classifier
Author(s) -
Vikrant Khadatkar,
Shubhangi Bagadkar,
Prof. Nutan Dhande
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.41100
Subject(s) - artificial intelligence , computer science , facial recognition system , support vector machine , biometrics , classifier (uml) , attendance , feature extraction , face detection , pattern recognition (psychology) , three dimensional face recognition , face recognition grand challenge , computer vision , machine learning , economics , economic growth
Face recognition system plays a vital role in almost every sector in this digital era. Face recognition is one of the popular biometrics’ techniques. It can used for security, authentication, identification and so on. It having low accuracy when compared to iris recognition and fingerprint recognition, but it is being widely used due to its contactless process. Face recognition technique can also be used for attendance marking field. This system targets to building a class attendance system which is used the technique of face recognition. We all know existing manual attendance system is taking more time and difficult to maintain. And having chances of proxy in attendance. That’s why, the need for this system occurs. This system consists of four phases- registration module, database creation, face detection, face recognition, attendance updating, attendance sending. Database consist of the image of the students in class. Face detection and recognition is performed using HOG feature extraction and SVM (Support Vector Machine) classifier. Faces will be detected and recognized from video streaming of the classroom. Attendance will be mailed to the respective faculty at the end of the lectures. Keywords: Face Recognition; Face Detection; SVM classifier; HOG feature extraction; attendance system;