Video based face recognition with limited resources

Abstract

Face recognition(FR) has multi-domain applications and video based FR is good area of research in terms of accuracy and performance. Recent research has proved that Convolutional Neural Network (CNN) is a one of best solution for object detection and recognition as it extracts features from on its own and performs classification as well. As we go for higher accuracy models, size of network increases and it requires more time to process a frame or video as it involves more computations. This paper aims at building a FR model which is smaller in network size, requires limited resources while building and still achieves good accuracy. The system uses combination of deep learning and machine learning based solution. FR system is built with CNN-Suport Vector Machine(SVM) model where CNN performs feature extraction and SVM performs classification task. Results shows that CNN-SVM model gives higher accuracy (94.05% validation and 90.17% testing) compared to conventional CNN-softmax model (93.37% validation and 88.77% testing) with a small network size and also requires less training time. Results can be improved by using cross validation techniques.