Convolutional neural network for face recognition with pose and illumination variation

Ahmad Radzi, Syafeeza and Mohamad, Khalil-Hani and Liew, Shan Sung and Bakhteri, Rabia (2014) Convolutional neural network for face recognition with pose and illumination variation. International Journal of Engineering and Technology (IJET), 6 (1). pp. 44-57. ISSN 0975-4024

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Convolutional Neural Network, face recognition, biometric identification, Stochastic Diagonal Levenberg-Marquardt
Divisions:	Faculty of Electronics and Computer Engineering > Department of Computer Engineering
Depositing User:	SYAFEEZA AHMAD RADZI
Date Deposited:	07 Mar 2014 04:08
Last Modified:	04 Jul 2023 15:43
URI:	http://eprints.utem.edu.my/id/eprint/11703
Statistic Details:	View Download Statistic

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