CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Learning low-rank representations with classwise block-diagonal structure for robust face recognition
Li, Yong1; Liu, Jing1; Li, Zechao2; Zhang, Yangmuzi3; Lu, Hanqing1; Ma, Songde1
2014
Conference NameAAAI
Source PublicationAAAI Conference on Artificial Intelligence
Pages2810-2816
Conference Date2014
Conference PlaceQuébec, Canada
Abstract
Face recognition has been widely studied due to its importance in various applications. However, the case that both training images and testing images are corrupted is not well addressed. Motivated by the success of low-rank matrix recovery, we propose a novel semi-supervised low-rank matrix recovery algorithm for robust face recognition. The proposed method can learn robust discriminative representations for both training images and testing images simultaneously by exploiting the classwise block-diagonal structure. Specifically, low-rank matrix approximation can handle the possible contamination of data. Moreover, the classwise block-diagonal structure is exploited to promote discrimination of representations for robust recognition. The above issues are formulated into a unified objective function and we design an efficient optimization procedure based on augmented Lagrange multiplier method to solve it. Extensive experiments on three public databases are performed to validate the effectiveness of our approach. The strong identification capability of representations with block-diagonal structure is verified.
 
KeywordClasswise Block-diagonal Structure Low-rank Representation
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/4692
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorLi, Yong
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.School of Computer Science, Nanjing University of Science and Technology
3.University of Maryland, College Park
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li, Yong,Liu, Jing,Li, Zechao,et al. Learning low-rank representations with classwise block-diagonal structure for robust face recognition[C],2014:2810-2816.
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