TY - GEN
T1 - Robust face recognition via facial disguise learning
AU - Yang, Meng
AU - Shen, Linlin
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2014.
PY - 2014
Y1 - 2014
N2 - The sparse representation based classifier (SRC) has been successfully applied to robust face recognition (FR) with various disguises. Following SRC, recently regularized robust coding (RRC) was proposed for more robustness to facial occlusion by designing a new robust representation residual term. Although RRC has achieved the leading performance, it ignores the prior knowledge embedded in facial disguises. In this paper, we proposed a novel facial disguise learning (FDL) model, in which the unknown occlusion pattern in the query image is learned using a collected disguise mask dictionary. Two learning strategies with an iterative reweighted coding algorithm, independent FDL and joint FDL, were presented in this paper. The experiments on face recognition with disguise clearly show the advantage of the proposed FDL in accuracy and efficiency.
AB - The sparse representation based classifier (SRC) has been successfully applied to robust face recognition (FR) with various disguises. Following SRC, recently regularized robust coding (RRC) was proposed for more robustness to facial occlusion by designing a new robust representation residual term. Although RRC has achieved the leading performance, it ignores the prior knowledge embedded in facial disguises. In this paper, we proposed a novel facial disguise learning (FDL) model, in which the unknown occlusion pattern in the query image is learned using a collected disguise mask dictionary. Two learning strategies with an iterative reweighted coding algorithm, independent FDL and joint FDL, were presented in this paper. The experiments on face recognition with disguise clearly show the advantage of the proposed FDL in accuracy and efficiency.
KW - Facial disguise learning
KW - Regularized robust coding
KW - Robust face recognition
UR - http://www.scopus.com/inward/record.url?scp=84914816427&partnerID=8YFLogxK
U2 - 10.1007/978-3-662-45643-9_33
DO - 10.1007/978-3-662-45643-9_33
M3 - Conference contribution
AN - SCOPUS:84914816427
T3 - Communications in Computer and Information Science
SP - 311
EP - 320
BT - Pattern Recognition - 6th Chinese Conference, CCPR 2014, Proceedings
A2 - Li, Shutao
A2 - Wang, Yaonan
A2 - Liu, Chenglin
PB - Springer Verlag
T2 - 6th Chinese Conference on Pattern Recognition, CCPR 2014
Y2 - 17 November 2014 through 19 November 2014
ER -