Biview face recognition in the shape-texture domain

Authors:
Bing Xiao;Xinbo Gao;Dacheng Tao;Xuelong Li
Affiliations:
School of Electronic Engineering, Xidian University, Xi'an 710071, PR China and School of Computer Science, Shaanxi Normal University, Xi'an 710062, PR China;School of Electronic Engineering, Xidian University, Xi'an 710071, PR China;Centre for Quantum Computation & Intelligent Systems, The Faculty of Engineering & Information Technology, University of Technology, Sydney, 235 Jones Street, Ultimo, NSW 2007, Australia;Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, ...
Venue:
Pattern Recognition
Year:
2013

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Abstract

Face recognition is one of the biometric identification methods with the highest potential. The existing face recognition algorithms relying on the texture information of face images are affected greatly by the variation of expression, scale and illumination. Whereas the algorithms based on the shape topology weaken the influence of illumination to some extent, but the impact of expression, scale and illumination on face recognition is still unsolved. To this end, we propose a new method for face recognition by integrating texture information with shape information, called biview face recognition algorithm. The texture models are constructed by using subspace learning methods and shape topologies are formed by building graphs for face images. The proposed biview face recognition method is compared with recognition algorithms merely based on texture or shape information. Experimental results of recognizing faces under the variation of illumination, expression and scale demonstrate that the performance of the proposed biview face recognition outperforms texture-based and shape-based algorithms.