Sample diversity, discriminative and comprehensive dictionary learning for face recognition

Guojun Lin; Meng Yang; Linlin Shen; Weicheng Xie; Zhonglong Zheng

doi:10.1007/978-3-319-46654-5_12

Sample diversity, discriminative and comprehensive dictionary learning for face recognition

Guojun Lin, Meng Yang, Linlin Shen, Weicheng Xie, Zhonglong Zheng

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

For face recognition, conventional dictionary learning (DL) methods have disadvantages. In the paper, we propose a novel robust, discriminative and comprehensive DL (RDCDL) model. The proposed model uses sample diversities of the same face image to make the dictionary robust. The model includes class-specific dictionary atoms and disturbance dictionary atoms, which can well represent the data from different classes. Both the dictionary and the representation coefficients of data on the dictionary introduce discriminative information, which improves effectively the discrimination capability of the dictionary. The proposed RDCDL is extensively evaluated on benchmark face image databases, and it shows superior performance to many state-of-the-art sparse representation and dictionary learning methods for face recognition.

Original language	English
Title of host publication	Biometric Recognition - 11th Chinese Conference, CCBR 2016, Proceedings
Editors	Shiguang Shan, Zhisheng You, Jie Zhou, Weishi Zheng, Yunhong Wang, Zhenan Sun, Jianjiang Feng, Qijun Zhao
Publisher	Springer Verlag
Pages	102-111
Number of pages	10
ISBN (Print)	9783319466538
DOIs	https://doi.org/10.1007/978-3-319-46654-5_12
Publication status	Published - 2016
Externally published	Yes
Event	11th Chinese Conference on Biometric Recognition, CCBR 2016 - Chengdu, China Duration: 14 Oct 2016 → 16 Oct 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9967 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th Chinese Conference on Biometric Recognition, CCBR 2016
Country/Territory	China
City	Chengdu
Period	14/10/16 → 16/10/16

Keywords

Dictionary learning
Face recognition
Sparse representation

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-46654-5_12

Cite this

Lin, G., Yang, M., Shen, L., Xie, W., & Zheng, Z. (2016). Sample diversity, discriminative and comprehensive dictionary learning for face recognition. In S. Shan, Z. You, J. Zhou, W. Zheng, Y. Wang, Z. Sun, J. Feng, & Q. Zhao (Eds.), Biometric Recognition - 11th Chinese Conference, CCBR 2016, Proceedings (pp. 102-111). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9967 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46654-5_12

Lin, Guojun ; Yang, Meng ; Shen, Linlin et al. / Sample diversity, discriminative and comprehensive dictionary learning for face recognition. Biometric Recognition - 11th Chinese Conference, CCBR 2016, Proceedings. editor / Shiguang Shan ; Zhisheng You ; Jie Zhou ; Weishi Zheng ; Yunhong Wang ; Zhenan Sun ; Jianjiang Feng ; Qijun Zhao. Springer Verlag, 2016. pp. 102-111 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Sample diversity, discriminative and comprehensive dictionary learning for face recognition",

abstract = "For face recognition, conventional dictionary learning (DL) methods have disadvantages. In the paper, we propose a novel robust, discriminative and comprehensive DL (RDCDL) model. The proposed model uses sample diversities of the same face image to make the dictionary robust. The model includes class-specific dictionary atoms and disturbance dictionary atoms, which can well represent the data from different classes. Both the dictionary and the representation coefficients of data on the dictionary introduce discriminative information, which improves effectively the discrimination capability of the dictionary. The proposed RDCDL is extensively evaluated on benchmark face image databases, and it shows superior performance to many state-of-the-art sparse representation and dictionary learning methods for face recognition.",

keywords = "Dictionary learning, Face recognition, Sparse representation",

author = "Guojun Lin and Meng Yang and Linlin Shen and Weicheng Xie and Zhonglong Zheng",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 11th Chinese Conference on Biometric Recognition, CCBR 2016 ; Conference date: 14-10-2016 Through 16-10-2016",

year = "2016",

doi = "10.1007/978-3-319-46654-5_12",

language = "English",

isbn = "9783319466538",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "102--111",

editor = "Shiguang Shan and Zhisheng You and Jie Zhou and Weishi Zheng and Yunhong Wang and Zhenan Sun and Jianjiang Feng and Qijun Zhao",

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}

Lin, G, Yang, M, Shen, L, Xie, W & Zheng, Z 2016, Sample diversity, discriminative and comprehensive dictionary learning for face recognition. in S Shan, Z You, J Zhou, W Zheng, Y Wang, Z Sun, J Feng & Q Zhao (eds), Biometric Recognition - 11th Chinese Conference, CCBR 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9967 LNCS, Springer Verlag, pp. 102-111, 11th Chinese Conference on Biometric Recognition, CCBR 2016, Chengdu, China, 14/10/16. https://doi.org/10.1007/978-3-319-46654-5_12

Sample diversity, discriminative and comprehensive dictionary learning for face recognition. / Lin, Guojun; Yang, Meng; Shen, Linlin et al.
Biometric Recognition - 11th Chinese Conference, CCBR 2016, Proceedings. ed. / Shiguang Shan; Zhisheng You; Jie Zhou; Weishi Zheng; Yunhong Wang; Zhenan Sun; Jianjiang Feng; Qijun Zhao. Springer Verlag, 2016. p. 102-111 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9967 LNCS).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Sample diversity, discriminative and comprehensive dictionary learning for face recognition

AU - Lin, Guojun

AU - Yang, Meng

AU - Shen, Linlin

AU - Xie, Weicheng

AU - Zheng, Zhonglong

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

Y1 - 2016

N2 - For face recognition, conventional dictionary learning (DL) methods have disadvantages. In the paper, we propose a novel robust, discriminative and comprehensive DL (RDCDL) model. The proposed model uses sample diversities of the same face image to make the dictionary robust. The model includes class-specific dictionary atoms and disturbance dictionary atoms, which can well represent the data from different classes. Both the dictionary and the representation coefficients of data on the dictionary introduce discriminative information, which improves effectively the discrimination capability of the dictionary. The proposed RDCDL is extensively evaluated on benchmark face image databases, and it shows superior performance to many state-of-the-art sparse representation and dictionary learning methods for face recognition.

AB - For face recognition, conventional dictionary learning (DL) methods have disadvantages. In the paper, we propose a novel robust, discriminative and comprehensive DL (RDCDL) model. The proposed model uses sample diversities of the same face image to make the dictionary robust. The model includes class-specific dictionary atoms and disturbance dictionary atoms, which can well represent the data from different classes. Both the dictionary and the representation coefficients of data on the dictionary introduce discriminative information, which improves effectively the discrimination capability of the dictionary. The proposed RDCDL is extensively evaluated on benchmark face image databases, and it shows superior performance to many state-of-the-art sparse representation and dictionary learning methods for face recognition.

KW - Dictionary learning

KW - Face recognition

KW - Sparse representation

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SN - 9783319466538

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 102

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BT - Biometric Recognition - 11th Chinese Conference, CCBR 2016, Proceedings

A2 - Shan, Shiguang

A2 - You, Zhisheng

A2 - Zhou, Jie

A2 - Zheng, Weishi

A2 - Wang, Yunhong

A2 - Sun, Zhenan

A2 - Feng, Jianjiang

A2 - Zhao, Qijun

PB - Springer Verlag

T2 - 11th Chinese Conference on Biometric Recognition, CCBR 2016

Y2 - 14 October 2016 through 16 October 2016

ER -

Lin G, Yang M, Shen L, Xie W, Zheng Z. Sample diversity, discriminative and comprehensive dictionary learning for face recognition. In Shan S, You Z, Zhou J, Zheng W, Wang Y, Sun Z, Feng J, Zhao Q, editors, Biometric Recognition - 11th Chinese Conference, CCBR 2016, Proceedings. Springer Verlag. 2016. p. 102-111. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46654-5_12

Sample diversity, discriminative and comprehensive dictionary learning for face recognition

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Keywords

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