Name
Title | ||
|---|---|---|
Adaptively weighted subpattern-based sparse preserving projection for face recognition |
Abstract | ||
|---|---|---|
In this paper, we propose an adaptively weighted subpattern-based sparse preserving projection (Aw-spSPP) algorithm for face recognition. Unlike SPP (Sparse preserving projection) based on a whole image pattern, the proposed AwSpSPP method operates on sub-patterns partitioned from an original whole face image and separately extracts corresponding local sub-features from them. Moreover, the contribution of each sub-pattern can be adaptively computed by sparse weights needless of additional parameter such as neighborhood size used in Aw-spLPP (adaptively weighted subpattern-based locality preserving projection). Experimental results on three bench mark face databases (ORL, YALE and PIE) show that Aw-spSPP can overcome the shortcomings of the existed subpattern-based methods and achieve promising recognition accuracy. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/FSKD.2012.6233711 | FSKD |
Keywords | Field | DocType |
pie database,recognition accuracy,local subfeature extraction,subpattern,aw-spspp algorithm,face recognition,face image pattern,yale database,sparse matrices,feature extraction,orl database,adaptively weighted subpattern-based locality preserving projection,aw-splpp,benchmark face databases,adaptively weighted subpattern-based sparse preserving projection,neighborhood size,sparse weights,sparse preserving projection,databases,principal component analysis,vectors,face | Facial recognition system,Benchmark (surveying),Locality,Pattern recognition,Computer science,Feature extraction,Artificial intelligence,Machine learning,Principal component analysis,Sparse matrix | Conference |
Volume | Issue | ISBN |
null | null | 978-1-4673-0025-4 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
2 | ||