Deep Sparse Representation Classifier for facial recognition and detection system

Cheng, EJ; Chou, KP; Rajora, S; Jin, BH; Tanveer, M; Lin, CT; Young, KY; Lin, WC; Prasad, M

Deep Sparse Representation Classifier for facial recognition and detection system

Cheng, EJ Chou, KP Rajora, S Jin, BH Tanveer, M Lin, CT

Young, KY Lin, WC Prasad, M

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Publication Type:: Journal Article
Citation:: Pattern Recognition Letters, 2019, 125 pp. 71 - 77
Issue Date:: 2019-07-01

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Field	Value	Language
dc.contributor.author	Cheng, EJ	en_US
dc.contributor.author	Chou, KP	en_US
dc.contributor.author	Rajora, S	en_US
dc.contributor.author	Jin, BH	en_US
dc.contributor.author	Tanveer, M	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Young, KY	en_US
dc.contributor.author	Lin, WC	en_US
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253	en_US
dc.date.issued	2019-07-01	en_US
dc.identifier.citation	Pattern Recognition Letters, 2019, 125 pp. 71 - 77	en_US
dc.identifier.issn	0167-8655	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134776
dc.description.abstract	© 2019 Elsevier B.V. This paper proposes a two-layer Convolutional Neural Network (CNN) to learn the high-level features which utilizes to the face identification via sparse representation. Feature extraction plays a vital role in real-world pattern recognition and classification tasks. The details description of the given input face image, significantly improve the performance of the facial recognition system. Sparse Representation Classifier (SRC) is a popular face classifier that sparsely represents the face image by a subset of training data, which is known as insensitive to the choice of feature space. The proposed method shows the performance improvement of SRC via a precisely selected feature exactor. The experimental results show that the proposed method outperforms other methods on given datasets.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP180100670
dc.relation	http://purl.org/au-research/grants/arc/DP180100656
dc.relation.ispartof	Pattern Recognition Letters	en_US
dc.relation.isbasedon	10.1016/j.patrec.2019.03.006	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Deep Sparse Representation Classifier for facial recognition and detection system	en_US
dc.type	Journal Article
utslib.citation.volume	125	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1702 Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	125	en_US

Abstract:

© 2019 Elsevier B.V. This paper proposes a two-layer Convolutional Neural Network (CNN) to learn the high-level features which utilizes to the face identification via sparse representation. Feature extraction plays a vital role in real-world pattern recognition and classification tasks. The details description of the given input face image, significantly improve the performance of the facial recognition system. Sparse Representation Classifier (SRC) is a popular face classifier that sparsely represents the face image by a subset of training data, which is known as insensitive to the choice of feature space. The proposed method shows the performance improvement of SRC via a precisely selected feature exactor. The experimental results show that the proposed method outperforms other methods on given datasets.

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http://hdl.handle.net/10453/134776