TY - GEN
T1 - Sparse Low-Rank Component Coding for Face Recognition with Illumination and Corruption
AU - Yang, Shicheng
AU - Wen, Ying
AU - He, Lianghua
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - Sparse representation-based classification shows a good performance for face recognition in recent years, but it can not be suitable for face recognition with illumination and corruption, which are often presented in the practical applications. To solve the problem, in this paper, we propose a novel SRC based method for face recognition named sparse low-rank component coding (SLC). In SLC, we utilize the low-rank component from training dataset to construct dictionary. The dictionary composed of low-rank component is able to describe the face feature better, especially for training samples with illumination and corruption. Our recognition rule is based on the minimum class-wise reconstruction residual which leads to a substantial improvement on the performance of SLC. Extensive experiments on benchmark face databases demonstrate that the proposed method consistently outperforms the other sparse representation based approaches for face recognition with illumination and corruption.
AB - Sparse representation-based classification shows a good performance for face recognition in recent years, but it can not be suitable for face recognition with illumination and corruption, which are often presented in the practical applications. To solve the problem, in this paper, we propose a novel SRC based method for face recognition named sparse low-rank component coding (SLC). In SLC, we utilize the low-rank component from training dataset to construct dictionary. The dictionary composed of low-rank component is able to describe the face feature better, especially for training samples with illumination and corruption. Our recognition rule is based on the minimum class-wise reconstruction residual which leads to a substantial improvement on the performance of SLC. Extensive experiments on benchmark face databases demonstrate that the proposed method consistently outperforms the other sparse representation based approaches for face recognition with illumination and corruption.
KW - Classification
KW - Face recognition
KW - Illumination and corruption
KW - Low-rank component
KW - Sparse representation
UR - https://www.scopus.com/pages/publications/85054200017
U2 - 10.1109/ICASSP.2018.8462114
DO - 10.1109/ICASSP.2018.8462114
M3 - 会议稿件
AN - SCOPUS:85054200017
SN - 9781538646588
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1693
EP - 1697
BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Y2 - 15 April 2018 through 20 April 2018
ER -