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Generalized compressed sensing with QR-based vision matrix learning for face recognition under natural scenes
Ji-xin Liu1; Guang Han1; Ning Sun1; Xiao-fei Li1; Zhi-guo Gong2; Quan-sen Sun3
2019-09-01
Source PublicationSignal Processing: Image Communication
ISSN0923-5965
Volume77Pages:11-19
Abstract

Face recognition under natural scenes is a significant challenge in pattern recognition research. With the success of sparse representation-based classification in related fields, face recognition based on compressed sensing (CS) theory has received increasing attention. These CS-based approaches produce excellent results when dealing with face data from experimental environments, but are inadequate when dealing with images from natural scenes. For solving this problem, a novel Generalized Compressed Sensing (GCS) framework is proposed in this paper. The main innovations of this paper are three-fold. First, with reference to the commutative property of the inner product, GCS recovery treats the original CS matrix, not the original signal, as the processing object. Second, in order to ensure the reliability and feasibility of GCS recovery, a QR-based vision matrix learning method is presented to realize face information embedding for the original CS matrix. Third, to balance the restricted isometry property (RIP) of the original CS matrix for CS sampling and its sparsity for GCS recovery, a low density parity check code is introduced to generate the original CS matrix. With this full CS framework including CS sampling and GCS recovery, the final generalized l-norm optimal solution can be used as the criterion for face recognition. Experimental results show that, compared with conventional approaches to CS recognition, the proposed method achieves a significant performance for face recognition tasks under natural scenes.

KeywordFace Recognition Generalized Compressed Sensing Low Density Parity Check Code Matrix Qr-based Vision Matrix Learning
DOI10.1016/j.image.2019.05.009
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000473840100002
Scopus ID2-s2.0-85066329761
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorJi-xin Liu
Affiliation1.Engineering Research Center of Wideband Wireless Communication Technology,Ministry of Education,Nanjing University of Posts and Telecommunications,Nanjing,210003,China
2.Faculty of Science and Technology,University of Macau,Macau,China
3.School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing,210094,China
Recommended Citation
GB/T 7714
Ji-xin Liu,Guang Han,Ning Sun,et al. Generalized compressed sensing with QR-based vision matrix learning for face recognition under natural scenes[J]. Signal Processing: Image Communication,2019,77:11-19.
APA Ji-xin Liu,Guang Han,Ning Sun,Xiao-fei Li,Zhi-guo Gong,&Quan-sen Sun.(2019).Generalized compressed sensing with QR-based vision matrix learning for face recognition under natural scenes.Signal Processing: Image Communication,77,11-19.
MLA Ji-xin Liu,et al."Generalized compressed sensing with QR-based vision matrix learning for face recognition under natural scenes".Signal Processing: Image Communication 77(2019):11-19.
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