Smart Attendance System Usign CNN

TL;DR

This paper presents a real-time face recognition attendance system using CNN to overcome lighting and noise issues, automatically updating records in Excel and MongoDB for efficient tracking in educational or office environments.

Contribution

Introduces a CNN-based face recognition system for attendance that improves accuracy over traditional methods and integrates real-time recording with database storage.

Findings

CNN outperforms Eigenfaces and Fisher faces under challenging conditions

System automates attendance recording with high accuracy

Database integration enables efficient record management

Abstract

The research on the attendance system has been going for a very long time, numerous arrangements have been proposed in the last decade to make this system efficient and less time consuming, but all those systems have several flaws. In this paper, we are introducing a smart and efficient system for attendance using face detection and face recognition. This system can be used to take attendance in colleges or offices using real-time face recognition with the help of the Convolution Neural Network(CNN). The conventional methods like Eigenfaces and Fisher faces are sensitive to lighting, noise, posture, obstruction, illumination etc. Hence, we have used CNN to recognize the face and overcome such difficulties. The attendance records will be updated automatically and stored in an excel sheet as well as in a database. We have used MongoDB as a backend database for attendance records.