DocumentCode :
595040
Title :
Optimal metric selection for improved multi-pose face recognition with group information
Author :
Xin Zhang ; Due-Son Pharn ; Wanquan Liu ; Venkatesh, Svetha
Author_Institution :
IMPCA, Curtin Univ., Bentley, WA, Australia
fYear :
2012
fDate :
11-15 Nov. 2012
Firstpage :
1675
Lastpage :
1678
Abstract :
We address the limitation of sparse representation based classification with group information for multi-pose face recognition. First, we observe that the key issue of such classification problem lies in the choice of the metric norm of the residual vectors, which represent the fitness of each class. Then we point out that limitation of the current sparse representation classification algorithms is the wrong choice of the ℓ2 norm, which does not match with data statistics as these residual values may be considerably non-Gaussian. We propose an explicit but effective solution using ℓp norm and explain theoretically and numerically why such metric norm would be able to suppress outliers and thus can significantly improve classification performance comparable to the state-of-arts algorithms on some challenging datasets.
Keywords :
face recognition; image classification; image representation; pose estimation; sparse matrices; classification performance; data statistics; group information; l2 norm; multipose face recognition; optimal metric selection; outlier suppression; residual vector metric norm; sparse representation classification algorithm; Face; Face recognition; Lighting; Measurement; Robustness; Training; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2012 21st International Conference on
Conference_Location :
Tsukuba
ISSN :
1051-4651
Print_ISBN :
978-1-4673-2216-4
Type :
conf
Filename :
6460470
Link To Document :
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