Deep Learning Based Face Recognition with Sparse Representation Classification

Eric Juwei Cheng; Mukesh Prasad; Deepak Puthal; Nabin Sharma; Om Kumar Prasad; Po Hao Chin; Chin Teng Lin; Michael Blumenstein

doi:10.1007/978-3-319-70090-8_67

Deep Learning Based Face Recognition with Sparse Representation Classification

Eric Juwei Cheng, Mukesh Prasad, Deepak Puthal, Nabin Sharma, Om Kumar Prasad, Po Hao Chin, Chin Teng Lin, Michael Blumenstein

Electrical Engineering

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

9 Scopus citations

Abstract

Feature extraction is an essential step in solving real-world pattern recognition and classification problems. The accuracy of face recognition highly depends on the extracted features to represent a face. The traditional algorithms uses geometric techniques, comprising feature values including distance and angle between geometric points (eyes corners, mouth extremities, and nostrils). These features are sensitive to the elements such as illumination, variation of poses, various expressions, to mention a few. Recently, deep learning techniques have been very effective for feature extraction, and deep features have considerable tolerance for various conditions and unconstrained environment. This paper proposes a two layer deep convolutional neural network (CNN) for face feature extraction and applied sparse representation for face identification. The sparsity and selectivity of deep features can strengthen sparseness for the solution of sparse representation, which generally improves the recognition rate. The proposed method outperforms other feature extraction and classification methods in terms of recognition accuracy.

Original language	British English
Title of host publication	Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
Editors	Derong Liu, Shengli Xie, El-Sayed M. El-Alfy, Dongbin Zhao, Yuanqing Li
Publisher	Springer Verlag
Pages	665-674
Number of pages	10
ISBN (Print)	9783319700892
DOIs	https://doi.org/10.1007/978-3-319-70090-8_67
State	Published - 2017
Event	24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China Duration: 14 Nov 2017 → 18 Nov 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10636 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th International Conference on Neural Information Processing, ICONIP 2017
Country/Territory	China
City	Guangzhou
Period	14/11/17 → 18/11/17

Keywords

Classification
Convolutional neural network
Deep learning
Face recognition
Feature extraction

Access to Document

10.1007/978-3-319-70090-8_67

Cite this

Cheng, E. J., Prasad, M., Puthal, D., Sharma, N., Prasad, O. K., Chin, P. H., Lin, C. T., & Blumenstein, M. (2017). Deep Learning Based Face Recognition with Sparse Representation Classification. In D. Liu, S. Xie, E.-S. M. El-Alfy, D. Zhao, & Y. Li (Eds.), Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings (pp. 665-674). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10636 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-70090-8_67

Cheng, Eric Juwei ; Prasad, Mukesh ; Puthal, Deepak et al. / Deep Learning Based Face Recognition with Sparse Representation Classification. Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. editor / Derong Liu ; Shengli Xie ; El-Sayed M. El-Alfy ; Dongbin Zhao ; Yuanqing Li. Springer Verlag, 2017. pp. 665-674 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{dabf665a59904fb49e7fa56e71ebf5d4,

title = "Deep Learning Based Face Recognition with Sparse Representation Classification",

abstract = "Feature extraction is an essential step in solving real-world pattern recognition and classification problems. The accuracy of face recognition highly depends on the extracted features to represent a face. The traditional algorithms uses geometric techniques, comprising feature values including distance and angle between geometric points (eyes corners, mouth extremities, and nostrils). These features are sensitive to the elements such as illumination, variation of poses, various expressions, to mention a few. Recently, deep learning techniques have been very effective for feature extraction, and deep features have considerable tolerance for various conditions and unconstrained environment. This paper proposes a two layer deep convolutional neural network (CNN) for face feature extraction and applied sparse representation for face identification. The sparsity and selectivity of deep features can strengthen sparseness for the solution of sparse representation, which generally improves the recognition rate. The proposed method outperforms other feature extraction and classification methods in terms of recognition accuracy.",

keywords = "Classification, Convolutional neural network, Deep learning, Face recognition, Feature extraction",

author = "Cheng, {Eric Juwei} and Mukesh Prasad and Deepak Puthal and Nabin Sharma and Prasad, {Om Kumar} and Chin, {Po Hao} and Lin, {Chin Teng} and Michael Blumenstein",

note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 24th International Conference on Neural Information Processing, ICONIP 2017 ; Conference date: 14-11-2017 Through 18-11-2017",

year = "2017",

doi = "10.1007/978-3-319-70090-8_67",

language = "British English",

isbn = "9783319700892",

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

publisher = "Springer Verlag",

pages = "665--674",

editor = "Derong Liu and Shengli Xie and El-Alfy, {El-Sayed M.} and Dongbin Zhao and Yuanqing Li",

booktitle = "Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings",

address = "Germany",

}

Cheng, EJ, Prasad, M, Puthal, D, Sharma, N, Prasad, OK, Chin, PH, Lin, CT & Blumenstein, M 2017, Deep Learning Based Face Recognition with Sparse Representation Classification. in D Liu, S Xie, E-SM El-Alfy, D Zhao & Y Li (eds), Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10636 LNCS, Springer Verlag, pp. 665-674, 24th International Conference on Neural Information Processing, ICONIP 2017, Guangzhou, China, 14/11/17. https://doi.org/10.1007/978-3-319-70090-8_67

Deep Learning Based Face Recognition with Sparse Representation Classification. / Cheng, Eric Juwei; Prasad, Mukesh; Puthal, Deepak et al.
Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. ed. / Derong Liu; Shengli Xie; El-Sayed M. El-Alfy; Dongbin Zhao; Yuanqing Li. Springer Verlag, 2017. p. 665-674 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10636 LNCS).

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

TY - GEN

T1 - Deep Learning Based Face Recognition with Sparse Representation Classification

AU - Cheng, Eric Juwei

AU - Prasad, Mukesh

AU - Puthal, Deepak

AU - Sharma, Nabin

AU - Prasad, Om Kumar

AU - Chin, Po Hao

AU - Lin, Chin Teng

AU - Blumenstein, Michael

PY - 2017

Y1 - 2017

N2 - Feature extraction is an essential step in solving real-world pattern recognition and classification problems. The accuracy of face recognition highly depends on the extracted features to represent a face. The traditional algorithms uses geometric techniques, comprising feature values including distance and angle between geometric points (eyes corners, mouth extremities, and nostrils). These features are sensitive to the elements such as illumination, variation of poses, various expressions, to mention a few. Recently, deep learning techniques have been very effective for feature extraction, and deep features have considerable tolerance for various conditions and unconstrained environment. This paper proposes a two layer deep convolutional neural network (CNN) for face feature extraction and applied sparse representation for face identification. The sparsity and selectivity of deep features can strengthen sparseness for the solution of sparse representation, which generally improves the recognition rate. The proposed method outperforms other feature extraction and classification methods in terms of recognition accuracy.

AB - Feature extraction is an essential step in solving real-world pattern recognition and classification problems. The accuracy of face recognition highly depends on the extracted features to represent a face. The traditional algorithms uses geometric techniques, comprising feature values including distance and angle between geometric points (eyes corners, mouth extremities, and nostrils). These features are sensitive to the elements such as illumination, variation of poses, various expressions, to mention a few. Recently, deep learning techniques have been very effective for feature extraction, and deep features have considerable tolerance for various conditions and unconstrained environment. This paper proposes a two layer deep convolutional neural network (CNN) for face feature extraction and applied sparse representation for face identification. The sparsity and selectivity of deep features can strengthen sparseness for the solution of sparse representation, which generally improves the recognition rate. The proposed method outperforms other feature extraction and classification methods in terms of recognition accuracy.

KW - Classification

KW - Convolutional neural network

KW - Deep learning

KW - Face recognition

KW - Feature extraction

UR - http://www.scopus.com/inward/record.url?scp=85035201605&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-70090-8_67

DO - 10.1007/978-3-319-70090-8_67

M3 - Conference contribution

AN - SCOPUS:85035201605

SN - 9783319700892

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

SP - 665

EP - 674

BT - Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings

A2 - Liu, Derong

A2 - Xie, Shengli

A2 - El-Alfy, El-Sayed M.

A2 - Zhao, Dongbin

A2 - Li, Yuanqing

PB - Springer Verlag

T2 - 24th International Conference on Neural Information Processing, ICONIP 2017

Y2 - 14 November 2017 through 18 November 2017

ER -

Cheng EJ, Prasad M, Puthal D, Sharma N, Prasad OK, Chin PH et al. Deep Learning Based Face Recognition with Sparse Representation Classification. In Liu D, Xie S, El-Alfy ESM, Zhao D, Li Y, editors, Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. Springer Verlag. 2017. p. 665-674. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-70090-8_67

Deep Learning Based Face Recognition with Sparse Representation Classification

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this