Improved OMP selecting sparse representation used with face recognition

Jian Zhang; Ke Yan; Zhenyu He

doi:10.1109/ICSESS.2014.6933637

Improved OMP selecting sparse representation used with face recognition

Jian Zhang^*
, Ke Yan
, Zhenyu He

^*Corresponding author for this work

Harbin Institute of Technology Shenzhen
Key Laboratory of Network Oriented Intelligent Computation

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

4 Citations (Scopus)

Abstract

With the worldwide strengthening of anti-terrorism and other identity verification, the products based on face recognition are used in real life more and more. The recognition as an important ways has become the focus of academic research in the world. Face recognition accuracy can be improved by increasing the number of training samples, but increasing number will result in a large computing complexity. In recent years, the sparse representation becomes hot in face recognition. In this paper, we propose an energy constraint orthogonal matching pursuit (ECOMP) algorithm for sparse representation in face recognition. It selects a few training samples and hierarchical structure for face recognition. In this method, we re-select training samples by ECOMP, calculate the weight of all the selected training samples and find the sparse training samples which can recover the test sample. While the AR and the ORL database experimental results show that this method has better performance than other identification methods.

Original language	English
Title of host publication	Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS
Editors	M. Surendra Prasad Babu, Li Wenzheng, Eric Tsui
Publisher	IEEE Computer Society
Pages	589-592
Number of pages	4
ISBN (Electronic)	9781479932788
DOIs	https://doi.org/10.1109/ICSESS.2014.6933637
Publication status	Published - 21 Oct 2014
Externally published	Yes
Event	2014 5th IEEE International Conference on Software Engineering and Service Science, ICSESS 2014 - Beijing, China Duration: 27 Jun 2014 → 29 Jun 2014

Publication series

Name	Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS
ISSN (Print)	2327-0586
ISSN (Electronic)	2327-0594

Conference

Conference	2014 5th IEEE International Conference on Software Engineering and Service Science, ICSESS 2014
Country/Territory	China
City	Beijing
Period	27/06/14 → 29/06/14

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

Keywords

image classification
orthogonal matching pursuit
sparse representation

Access to Document

10.1109/ICSESS.2014.6933637

Cite this

Zhang, J., Yan, K., & He, Z. (2014). Improved OMP selecting sparse representation used with face recognition. In M. S. Prasad Babu, L. Wenzheng, & E. Tsui (Eds.), Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS (pp. 589-592). Article 6933637 (Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS). IEEE Computer Society. https://doi.org/10.1109/ICSESS.2014.6933637

Zhang, Jian ; Yan, Ke ; He, Zhenyu. / Improved OMP selecting sparse representation used with face recognition. Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS. editor / M. Surendra Prasad Babu ; Li Wenzheng ; Eric Tsui. IEEE Computer Society, 2014. pp. 589-592 (Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS).

@inproceedings{b1e38ee19ee04a088799a50f629f836b,

title = "Improved OMP selecting sparse representation used with face recognition",

abstract = "With the worldwide strengthening of anti-terrorism and other identity verification, the products based on face recognition are used in real life more and more. The recognition as an important ways has become the focus of academic research in the world. Face recognition accuracy can be improved by increasing the number of training samples, but increasing number will result in a large computing complexity. In recent years, the sparse representation becomes hot in face recognition. In this paper, we propose an energy constraint orthogonal matching pursuit (ECOMP) algorithm for sparse representation in face recognition. It selects a few training samples and hierarchical structure for face recognition. In this method, we re-select training samples by ECOMP, calculate the weight of all the selected training samples and find the sparse training samples which can recover the test sample. While the AR and the ORL database experimental results show that this method has better performance than other identification methods.",

keywords = "image classification, orthogonal matching pursuit, sparse representation",

author = "Jian Zhang and Ke Yan and Zhenyu He",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 5th IEEE International Conference on Software Engineering and Service Science, ICSESS 2014 ; Conference date: 27-06-2014 Through 29-06-2014",

year = "2014",

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doi = "10.1109/ICSESS.2014.6933637",

language = "English",

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booktitle = "Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS",

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Zhang, J, Yan, K & He, Z 2014, Improved OMP selecting sparse representation used with face recognition. in MS Prasad Babu, L Wenzheng & E Tsui (eds), Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS., 6933637, Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS, IEEE Computer Society, pp. 589-592, 2014 5th IEEE International Conference on Software Engineering and Service Science, ICSESS 2014, Beijing, China, 27/06/14. https://doi.org/10.1109/ICSESS.2014.6933637

Improved OMP selecting sparse representation used with face recognition. / Zhang, Jian; Yan, Ke; He, Zhenyu.
Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS. ed. / M. Surendra Prasad Babu; Li Wenzheng; Eric Tsui. IEEE Computer Society, 2014. p. 589-592 6933637 (Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS).

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

TY - GEN

T1 - Improved OMP selecting sparse representation used with face recognition

AU - Zhang, Jian

AU - Yan, Ke

AU - He, Zhenyu

PY - 2014/10/21

Y1 - 2014/10/21

N2 - With the worldwide strengthening of anti-terrorism and other identity verification, the products based on face recognition are used in real life more and more. The recognition as an important ways has become the focus of academic research in the world. Face recognition accuracy can be improved by increasing the number of training samples, but increasing number will result in a large computing complexity. In recent years, the sparse representation becomes hot in face recognition. In this paper, we propose an energy constraint orthogonal matching pursuit (ECOMP) algorithm for sparse representation in face recognition. It selects a few training samples and hierarchical structure for face recognition. In this method, we re-select training samples by ECOMP, calculate the weight of all the selected training samples and find the sparse training samples which can recover the test sample. While the AR and the ORL database experimental results show that this method has better performance than other identification methods.

AB - With the worldwide strengthening of anti-terrorism and other identity verification, the products based on face recognition are used in real life more and more. The recognition as an important ways has become the focus of academic research in the world. Face recognition accuracy can be improved by increasing the number of training samples, but increasing number will result in a large computing complexity. In recent years, the sparse representation becomes hot in face recognition. In this paper, we propose an energy constraint orthogonal matching pursuit (ECOMP) algorithm for sparse representation in face recognition. It selects a few training samples and hierarchical structure for face recognition. In this method, we re-select training samples by ECOMP, calculate the weight of all the selected training samples and find the sparse training samples which can recover the test sample. While the AR and the ORL database experimental results show that this method has better performance than other identification methods.

KW - image classification

KW - orthogonal matching pursuit

KW - sparse representation

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M3 - Conference contribution

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T3 - Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS

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BT - Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS

A2 - Prasad Babu, M. Surendra

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A2 - Tsui, Eric

PB - IEEE Computer Society

T2 - 2014 5th IEEE International Conference on Software Engineering and Service Science, ICSESS 2014

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ER -

Zhang J, Yan K, He Z. Improved OMP selecting sparse representation used with face recognition. In Prasad Babu MS, Wenzheng L, Tsui E, editors, Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS. IEEE Computer Society. 2014. p. 589-592. 6933637. (Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS). doi: 10.1109/ICSESS.2014.6933637

Improved OMP selecting sparse representation used with face recognition

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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