Facial Recognition Based on Student Attendance Using Convolution Neural Network on Local Binary Pattern

Abstract

 Student attendance management system deal with the maintenance of the student’s attendance details. It generates the attendance of the student on basis of presence in class. It is maintained daily on the basis of attendance, only if the student is present, the attendance will be calculated. This application requires correct feed on input into the respective field. So the user find it difficult to use. The student attendance report for every month will be generated and consolidated. Attendance Management System is software developed for maintenance of student attendance in schools, colleges and institutes. It facilitates to access the attendance information of a student in a class. The information is sorted by the operators, which will be provided to the teacher for a class. The purpose of developing Attendance Management System is to computerized the way of taking attendance. The existing systems sometimes fail to recognize every faces of the student present in the classroom. Our project has been implemented in such a way to overcome this issue and it generates automatic attendance sheet at the end of the each session or in the between of the session as they require using Local Binary Patterns Histogram. Local Binary Patterns compute a local representation of texture. Local Binary Pattern (LBP) is a simple yet very efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number.