Kernel Coupled Cross-Regression for Low-Resolution Face Recognition
Joint Authors
Wang, Zhifei
Wan, Yanli
Tang, Zhen
Miao, Zhenjiang
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-06-13
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wang, Zhifei& Miao, Zhenjiang& Wan, Yanli& Tang, Zhen. 2013. Kernel Coupled Cross-Regression for Low-Resolution Face Recognition. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1031696
Modern Language Association (MLA)
Wang, Zhifei…[et al.]. Kernel Coupled Cross-Regression for Low-Resolution Face Recognition. Mathematical Problems in Engineering No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-1031696
American Medical Association (AMA)
Wang, Zhifei& Miao, Zhenjiang& Wan, Yanli& Tang, Zhen. Kernel Coupled Cross-Regression for Low-Resolution Face Recognition. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1031696
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1031696