22 May 2013 Coupled cross-regression for low-resolution face recognition
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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.
© 2013 SPIE and IS&T
Zhifei Wang, Zhifei Wang, Zhenjiang Miao, Zhenjiang Miao, Yanli Wan, Yanli Wan, Zhen Tang, 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 . Submission:

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