Automated Attendance System Based on Facial Recognition using Viola-Jones Algorithm

Abstract

Facial Recognition is the most used technology nowadays. Apart from Bio-metrics, Iris scan, fingerprint recognition methodologies, facial recognition is emerging recognition methodology these days. One of the most effective applications of this methodology is automated attendance using facial recognition, which is contact less, secure, and effective unlike in tradition way (manual attendance) it saves more time. Methodology used in this project involves Viola-Jones algorithm for face detection and Eigenfaces approach for feature selection and classification. In Viola-jones algorithm inputs are taken as captured images of individual persons and produce a dataset containing cropped images of individual and these dataset is directed to Eigenfaces approach as input and training of data occurs through the process of calculating eigen vectors for each eigenface. At the time of testing, Euclidean distance between eigen vectors of testing image and eigen vectors of trained eigen faces determines the matched individual. Facial recognition can also be done with PCA, which has 79.6 percent accuracy, and LBPH, which has 90.23 percent accuracy. However, when employing the Eigenfaces technique, the accuracy is 93.07 percent. MATLAB software with Computer Vision Toolbox and Deep Learning Toolbox is used for this work.