Metaface learning for sparse representation based face recognition

Meng Yang; Lei Zhang; Jian Yang; Dapeng Zhang

doi:10.1109/ICIP.2010.5652363

Metaface learning for sparse representation based face recognition

Meng Yang, Lei Zhang, Jian Yang, Dapeng Zhang

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

343 Citations (Scopus)

Abstract

Face recognition (FR) is an active yet challenging topic in computer vision applications. As a powerful tool to represent high dimensional data, recently sparse representation based classification (SRC) has been successfully used for FR. This paper discusses the metaface learning (MFL) of face images under the framework of SRC. Although directly using the training samples as dictionary bases can achieve good FR performance, a well learned dictionary matrix can lead to higher FR rate with less dictionary atoms. An SRC oriented unsupervised MFL algorithm is proposed in this paper and the experimental results on benchmark face databases demonstrated the improvements brought by the proposed MFL algorithm over original SRC.

Original language	English
Title of host publication	2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages	1601-1604
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2010.5652363
Publication status	Published - 1 Dec 2010
Event	2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong Duration: 26 Sept 2010 → 29 Sept 2010

Conference

Conference	2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/Territory	Hong Kong
City	Hong Kong
Period	26/09/10 → 29/09/10

Keywords

Face recognition
Metaface learning
Sparse representation

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

More information

10.1109/ICIP.2010.5652363

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title = "Metaface learning for sparse representation based face recognition",

abstract = "Face recognition (FR) is an active yet challenging topic in computer vision applications. As a powerful tool to represent high dimensional data, recently sparse representation based classification (SRC) has been successfully used for FR. This paper discusses the metaface learning (MFL) of face images under the framework of SRC. Although directly using the training samples as dictionary bases can achieve good FR performance, a well learned dictionary matrix can lead to higher FR rate with less dictionary atoms. An SRC oriented unsupervised MFL algorithm is proposed in this paper and the experimental results on benchmark face databases demonstrated the improvements brought by the proposed MFL algorithm over original SRC.",

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

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AU - Zhang, Dapeng

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AB - Face recognition (FR) is an active yet challenging topic in computer vision applications. As a powerful tool to represent high dimensional data, recently sparse representation based classification (SRC) has been successfully used for FR. This paper discusses the metaface learning (MFL) of face images under the framework of SRC. Although directly using the training samples as dictionary bases can achieve good FR performance, a well learned dictionary matrix can lead to higher FR rate with less dictionary atoms. An SRC oriented unsupervised MFL algorithm is proposed in this paper and the experimental results on benchmark face databases demonstrated the improvements brought by the proposed MFL algorithm over original SRC.

KW - Face recognition

KW - Metaface learning

KW - Sparse representation

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DO - 10.1109/ICIP.2010.5652363

M3 - Conference article published in proceeding or book

SN - 9781424479948

SP - 1601

EP - 1604

BT - 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings

T2 - 2010 17th IEEE International Conference on Image Processing, ICIP 2010

Y2 - 26 September 2010 through 29 September 2010

ER -

Metaface learning for sparse representation based face recognition

Abstract

Conference

Keywords

ASJC Scopus subject areas

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