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
History
Event
International Conference on Pattern Recognition (21st : 2012 : Tsukuba Science City, Japan)
Pagination
1675 - 1678
Publisher
ICPR Organizing Committee
Location
Tsubuka Science City, Japan
Place of publication
Tsubuka Science City, Japan
Start date
2012-11-11
End date
2012-11-15
ISBN-13
9784990644109
Language
eng
Publication classification
E1 Full written paper - refereed
Title of proceedings
ICPR 2012 : Proceedings of 21st International Conference on Pattern Recognition