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Efficient Attendance Management System

Author(s):

Shital Masalkar , Department of Computer Engineering University of Pune, Pune Maharashtra-India; Vinod Badgujar, Department of Computer Engineering University of Pune, Pune Maharashtra-India; Rajshri Zingade, Department of Computer Engineering University of Pune, Pune Maharashtra-India; Gayatri Raina, Department of Computer Engineering University of Pune, Pune Maharashtra-India; Rohini Bhujbal, Department of Computer Engineering University of Pune, Pune Maharashtra-India

Keywords:

Face Detection, Face Recognition, Eigen Faces, Data Base

Abstract

Now a days the smart attendance management system using face detection techniques. Daily attendance marking is a common and important activity in schools and colleges for checking the performance of students. Manual Attendance maintaining is difficult process, especially for large group of students. Some automated systems developed to overcome these difficulties, have drawbacks like cost, fake attendance, accuracy, intrusiveness. To overcome these drawbacks, there is need of smart and automated attendance system. We are implementing attendance system using face recognition. Since face is unique identity of person, the issue of fake attendance and proxies can be solved. The system uses local binary pattern face recognition technique as it is fast, simple and has greater success rate. Also, it has pro-vision to deal with intensity of light problem and head pose problem which makes it effective. This smart system can be an effective way to maintain the will-less squatter recognition system is proposed based on appearance-based features that focus on the un shorten squatter image rather than local facial features. The rest step in squatter recognition system is squatter detection Viola-Jones upper detection rates is used. The whole squatter recognition process can be divided into two parts squatter detection and squatter identification. For face detection part, Viola Jones face detection method has been used out of several face detection methods. After face detection, face is cropped from the actual image to remove the background. Eigen faces and sher faces methods have been used for face identification part. Average images of subjects have been used as training set to improve the accuracy of identification.

Other Details

Paper ID: IJSRDV7I30483
Published in: Volume : 7, Issue : 3
Publication Date: 01/06/2019
Page(s): 696-699

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