TY - GEN
T1 - Single-sample face recognition based on wssrc and expanding sample
AU - Xu, Zhijing
AU - Ye, Li
AU - He, Xiangjian
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - This paper proposes a face recognition method with one training image per person, and it is based on compressed sensing. We apply nonlinear dimensionality reduction through locally linear embedding and sparse coefficients to generate multiple samples of each person. These generated samples have multi-expressions and multi-gestures are added to the original sample set for training. Then, a super sparse random projection and weighted optimization are applied to improve the SRC. This proposed method is named weighted super sparse representation classification (WSSRC) and is used for face recognition in this paper. Experiments on the well-known ORL face dataset and FERET face dataset show that WSSRC is about 15.53 % and 7.67 %, respectively, more accurate than the original SRC method in the context of single sample face recognition problem. In addition, extensive experimental results reported in this paper show that WSSRC also achieve higher recognition rates than RSRC, SSRC DMMA, and DCT-based DMMA.
AB - This paper proposes a face recognition method with one training image per person, and it is based on compressed sensing. We apply nonlinear dimensionality reduction through locally linear embedding and sparse coefficients to generate multiple samples of each person. These generated samples have multi-expressions and multi-gestures are added to the original sample set for training. Then, a super sparse random projection and weighted optimization are applied to improve the SRC. This proposed method is named weighted super sparse representation classification (WSSRC) and is used for face recognition in this paper. Experiments on the well-known ORL face dataset and FERET face dataset show that WSSRC is about 15.53 % and 7.67 %, respectively, more accurate than the original SRC method in the context of single sample face recognition problem. In addition, extensive experimental results reported in this paper show that WSSRC also achieve higher recognition rates than RSRC, SSRC DMMA, and DCT-based DMMA.
KW - Local neighborhood embedding
KW - Nonlinear dimensionality reduction
KW - Single sample
KW - Sparse representation classification
KW - WSSRC
UR - http://www.scopus.com/inward/record.url?scp=84952316240&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-26181-2_18
DO - 10.1007/978-3-319-26181-2_18
M3 - Conference contribution
AN - SCOPUS:84952316240
SN - 9783319261805
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 197
EP - 206
BT - Multi-disciplinary Trends in Artificial Intelligence - 9th International Workshop, MIWAI 2015, Proceedings
A2 - Zheng, Xianghan
A2 - Bikakis, Antonis
PB - Springer Verlag
T2 - 9th International Workshop on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2015
Y2 - 13 November 2015 through 15 November 2015
ER -