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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 153790, 9 pages
Research Article

Kernel Coupled Cross-Regression for Low-Resolution Face Recognition

1Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China
2Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China

Received 25 March 2013; Accepted 28 May 2013

Academic Editor: Rafael Martinez-Guerra

Copyright © 2013 Zhifei Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Low resolution (LR) in face recognition (FR) surveillance applications will cause the problem of dimensional mismatch between LR image and its high-resolution (HR) template. In this paper, a novel method called kernel coupled cross-regression (KCCR) is proposed to deal with this problem. Instead of processing in the original observing space directly, KCCR projects LR and HR face images into a unified nonlinear embedding feature space using kernel coupled mappings and graph embedding. Spectral regression is further employed to improve the generalization performance and reduce the time complexity. Meanwhile, cross-regression is developed to fully utilize the HR embedding to increase the information of the LR space, thus to improve the recognition performance. Experiments on the FERET and CMU PIE face database show that KCCR outperforms the existing structure-based methods in terms of recognition rate as well as time complexity.