File(s) under permanent embargo
Optimal metric selection for improved multi-pose face recognition with group information
conference contribution
posted on 2012-01-01, 00:00 authored by X Zhang, D S Pham, W Liu, Svetha VenkateshSvetha VenkateshWe 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 - 1678Publisher
ICPR Organizing CommitteeLocation
Tsubuka Science City, JapanPlace of publication
Tsubuka Science City, JapanStart date
2012-11-11End date
2012-11-15ISBN-13
9784990644109Language
engPublication classification
E1 Full written paper - refereedTitle of proceedings
ICPR 2012 : Proceedings of 21st International Conference on Pattern RecognitionUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC