An Attendance System Based on Face Recognition and Detection Using CNN

Abstract

Face recognition is a biological tool that may be used to recognize people by their distinctive facial features. Numerous applications, including security, enforcement agencies, and consumer electronics, have made use of this technology. Face detection and recognition system works by analyzing a person's individual facial features to a database of recognized faces. The system can recognize the person if there is a match. We employ a variety of already trained models, such as those for image processing, pattern recognition, and artificial neural network method (ANN). The accuracy of facial recognition has been considerably improved by the advent of deep learning technologies. CNNs (Convolutional Neural Networks) with deep learning have significantly improved picture categorization. The Computer Vision and Machine Learning Group's deep learning model, DeepID, has achieved recognition rate of 98% on the ORL database for attendance systems. Key Words: Face Recognition, Biometric, Image processing, Pattern Recognition, Artificial Neural Network