Share Email Print

Journal of Electronic Imaging

Coupled cross-regression for low-resolution face recognition
Author(s): Zhifei Wang; Zhenjiang Miao; Yanli Wan; Zhen Tang
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 22 May 2013
PDF: 7 pages
J. Electron. Imag. 22(2) 023015 doi: 10.1117/1.JEI.22.2.023015
Published in: Journal of Electronic Imaging Volume 22, Issue 2
Show Author Affiliations
Zhifei Wang, Beijing Jiaotong Univ. (China)
Zhenjiang Miao, Beijing Jiaotong Univ. (China)
Yanli Wan, Beijing Jiaotong Univ. (China)
Zhen Tang, Beijing Jiaotong Univ. (China)

© SPIE. Terms of Use
Back to Top