Bilinear Regression Based Expression Face Neutralization Preserving Person-Specific Characters
CHEN Ying1,2, ZHANG Long-Yuan1, YI Xiao-Bin1
1.Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education,Jiangnan University, Wuxi 214122 2.Key Laboratory of System Control and Information Processing of Ministry of Education,Shanghai Jiao Tong University, Shanghai 200240
Abstract：In traditional learning-based expression neutralization methods, person-specific characters are removed from the visual neutral face.To solve this problem, an expression neutralization algorithm based on bilinear kernel rank reduced regression (BKRRR) is proposed. The BKRRR algorithm is designed to synthesize virtual expression and virtual neutral images from training samples simultaneously. An expression mask is established using differences of the two images. The test expression image is warped to neutral template by piece-wise affine warp. An image fusion strategy based on Poisson equation is then designed. The visual BKRRR neutral image is used as foreground, the warped image as background and the expression mask as foreground mask. And thus the visual neutralized face image with person-specific characters preserved is obtained. Experimental results show that the synthesized visual neutral image outperforms other learning-based methods in both visual and objective evaluation, and the accuracy of expression invariant face recognition is improved.
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2014, Vol. 27 
