Coupled cross-regression for low-resolution face recognition

Zhifei Wang; Zhenjiang Miao; Yanli Wan; Zhen Tang

doi:10.1117/1.JEI.22.2.023015

22 May 2013 Coupled cross-regression for low-resolution face recognition

Zhifei Wang, Zhenjiang Miao, Yanli Wan, Zhen Tang

Author Affiliations +

Journal of Electronic Imaging, Vol. 22, Issue 2, 023015 (May 2013). https://doi.org/10.1117/1.JEI.22.2.023015

Abstract

Dimensional mismatch between a low-resolution (LR) surveillance face image and its high-resolution (HR) template makes recognition very difficult. A novel method called coupled cross-regression (CCR) is proposed to deal with this problem. Instead of processing in the original observing space directly, CCR projects LR and HR face images into a unified low-embedding feature space. Spectral regression is employed to improve generalization performance and reduce computational complexity. Meanwhile, cross-regression is developed to utilize HR embedding to increase the information of the LR space, thus improving the recognition performance. Experiments on the FERET and CMU PIE face database show that CCR outperforms existing structure-based methods in terms of recognition rate as well as computational complexity.

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Zhifei Wang, Zhenjiang Miao, Yanli Wan, and Zhen Tang "Coupled cross-regression for low-resolution face recognition," Journal of Electronic Imaging 22(2), 023015 (22 May 2013). https://doi.org/10.1117/1.JEI.22.2.023015

Published: 22 May 2013

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