Convolutional neural network for face recognition with pose and illumination variation

• Main Authors: Ahmad Radzi, Syafeeza, Mohamad, Khalil-Hani, Liew, Shan Sung, Bakhteri, Rabia
• Format: Article
• Language: English
• Published: Engg Journals Publications 2014
• Online Access: http://eprints.utem.edu.my/id/eprint/11703/1/IJET14-06-01-041.pdf; http://eprints.utem.edu.my/id/eprint/11703/; https://www.enggjournals.com/ijet/docs/IJET14-06-01-041.pdf

Face recognition remains a challenging problem till today. The main challenge is how to improve the recognition performance when affected by the variability of non-linear effects that include illumination variances, poses, facial expressions, occlusions, etc. In this paper, a robust 4-layer Convolutional Neural Network (CNN) architecture is proposed for the face recognition problem, with a solution that is capable of handling facial images that contain occlusions, poses, facial expressions and varying illumination. Experimental results show that the proposed CNN solution outperforms existing works, achieving 99.5% recognition accuracy on AR database. The test on the 35-subjects of FERET database achieves an accuracy of 85.13%, which is in the similar range of performance as the best result of previous works. More significantly, our proposed system completes the facial recognition process in less than 0.01 seconds.