NDLI: Relaxed collaborative representation for face recognition based low-rank matrix recovery

Content Provider	IEEE Xplore Digital Library
Author	Khaji, R. Hong Li Hasan, T.M. Hongfeng Li Ali, Q.
Copyright Year	2014
Description	Author affiliation: Sch. of Math. & Stat., Huazhong Univ. of Sci. & Technol., Wuhan, China (Khaji, R.; Hong Li; Hongfeng Li) \|\| Dept. of Comput., Diyala Univ., Diyala, Iraq (Hasan, T.M.) \|\| Dept. of Electron. & Inf. Eng., Huazhong Univ. of Sci. & Technol., Wuhan, China (Ali, Q.)
Abstract	Face recognition is of paramount importance in computer vision and biometrics systems. In this paper we propose an improved method which is suitable to handle variations in image configurations like pose, illumination, and facial expressions as well as occlusion and disguise, in order to provide high efficiencyi in the face recognition. This method integrates the low-rank matrix which is recovered by using robust principal component analysis (RPCA) with relaxed collaborative representation (RCR). Low-rank representation allows us to better discriminate information which benefits to face identification, and R-CR contributes to the reduction of the variance of coding vector after coding each feature vector on its associated dictionary to allow flexibility of feature coding, thus addressing the similarity among features. Furthermore, it is characterized by the exploitation of the distinctiveness of different features by weighting its distance to other features in the coding domain. The effectiveness of the proposed method is validated by extensive experiments on different benchmark face databases.
Starting Page	50
Ending Page	55
File Size	5445419
Page Count	6
File Format	PDF
ISBN	9781479942121
ISSN	21585695
e-ISBN	9781479942114
DOI	10.1109/ICWAPR.2014.6961289
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	tion Databases Face recognition Face Recognition; PRCA; Relaxed Collaborative Representa Collaboration Lighting Sparse Representation Low-Rank Matrix Encoding Face Sparse matrices
Content Type	Text
Resource Type	Article

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