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Mathematical Problems in Engineering
Volume 2014, Article ID 724978, 10 pages
Research Article

An Improved Metric Learning Approach for Degraded Face Recognition

1Information Research Institute of Shandong Academy of Sciences, Jinan 250014, China
2College of Automation, Harbin Engineering University, Harbin 150001, China

Received 30 July 2013; Revised 20 December 2013; Accepted 20 December 2013; Published 11 February 2014

Academic Editor: Chung-Hao Chen

Copyright © 2014 Guofeng Zou 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.


To solve the matching problem of the elements in different data collections, an improved coupled metric learning approach is proposed. First, we improved the supervised locality preserving projection algorithm and added the within-class and between-class information of the improved algorithm to coupled metric learning, so a novel coupled metric learning method is proposed. Furthermore, we extended this algorithm to nonlinear space, and the kernel coupled metric learning method based on supervised locality preserving projection is proposed. In kernel coupled metric learning approach, two elements of different collections are mapped to the unified high dimensional feature space by kernel function, and then generalized metric learning is performed in this space. Experiments based on Yale and CAS-PEAL-R1 face databases demonstrate that the proposed kernel coupled approach performs better in low-resolution and fuzzy face recognition and can reduce the computing time; it is an effective metric method.