Feature correlation filter for face recognition

Xiangxin Zhu; Shengcai Liao; Zhen Lei; Rong Liu; Stan Z. Li

doi:10.1007/978-3-540-74549-5_9

Feature correlation filter for face recognition

Xiangxin Zhu, Shengcai Liao, Zhen Lei, Rong Liu, Stan Z. Li

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

12 Citations (Scopus)

Abstract

The correlation filters for pattern recognition, have been extensively studied in the areas of automatic target recognition(ATR) and biometrics. Whereas the conventional correlation filters perform directly on image pixels, in this paper, we propose a novel method, called "feature correlation filter (FCF)", by extending the concept of correlation filter to feature spaces. The FCF preserves the benefits of conventional correlation filters, i.e., shift-invariant, occlusion-insensitive, and closed-form solution, and also inherits virtues of the feature representations. Moreover, since the size of feature is often much smaller than the size of image, the FCF method can significantly reduce the storage requirement in recognition system. The comparative results on CMU-PIE and the FRGC2.0 database show that the proposed FCFs can achieve noteworthy performance improvement compared with their conventional counterpart.

Original language	English
Title of host publication	Advances in Biometrics - International Conference, ICB 2007, Proceedings
Publisher	Springer Verlag
Pages	77-86
Number of pages	10
ISBN (Print)	9783540745488
DOIs	https://doi.org/10.1007/978-3-540-74549-5_9
Publication status	Published - 2007
Externally published	Yes
Event	2007 International Conference on Advances in Biometrics, ICB 2007 - Seoul, Korea, Republic of Duration: Aug 27 2007 → Aug 29 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4642 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2007 International Conference on Advances in Biometrics, ICB 2007
Country/Territory	Korea, Republic of
City	Seoul
Period	8/27/07 → 8/29/07

Keywords

Correlation filters
Face recognition
Feature correlation filters
MACE

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-540-74549-5_9

Cite this

Zhu, X., Liao, S., Lei, Z., Liu, R., & Li, S. Z. (2007). Feature correlation filter for face recognition. In Advances in Biometrics - International Conference, ICB 2007, Proceedings (pp. 77-86). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4642 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-540-74549-5_9

Feature correlation filter for face recognition. / Zhu, Xiangxin; Liao, Shengcai; Lei, Zhen et al.
Advances in Biometrics - International Conference, ICB 2007, Proceedings. Springer Verlag, 2007. p. 77-86 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4642 LNCS).

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

Zhu, X, Liao, S, Lei, Z, Liu, R & Li, SZ 2007, Feature correlation filter for face recognition. in Advances in Biometrics - International Conference, ICB 2007, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4642 LNCS, Springer Verlag, pp. 77-86, 2007 International Conference on Advances in Biometrics, ICB 2007, Seoul, Korea, Republic of, 8/27/07. https://doi.org/10.1007/978-3-540-74549-5_9

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title = "Feature correlation filter for face recognition",

abstract = "The correlation filters for pattern recognition, have been extensively studied in the areas of automatic target recognition(ATR) and biometrics. Whereas the conventional correlation filters perform directly on image pixels, in this paper, we propose a novel method, called {"}feature correlation filter (FCF){"}, by extending the concept of correlation filter to feature spaces. The FCF preserves the benefits of conventional correlation filters, i.e., shift-invariant, occlusion-insensitive, and closed-form solution, and also inherits virtues of the feature representations. Moreover, since the size of feature is often much smaller than the size of image, the FCF method can significantly reduce the storage requirement in recognition system. The comparative results on CMU-PIE and the FRGC2.0 database show that the proposed FCFs can achieve noteworthy performance improvement compared with their conventional counterpart.",

keywords = "Correlation filters, Face recognition, Feature correlation filters, MACE",

author = "Xiangxin Zhu and Shengcai Liao and Zhen Lei and Rong Liu and Li, {Stan Z.}",

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AU - Liu, Rong

AU - Li, Stan Z.

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N2 - The correlation filters for pattern recognition, have been extensively studied in the areas of automatic target recognition(ATR) and biometrics. Whereas the conventional correlation filters perform directly on image pixels, in this paper, we propose a novel method, called "feature correlation filter (FCF)", by extending the concept of correlation filter to feature spaces. The FCF preserves the benefits of conventional correlation filters, i.e., shift-invariant, occlusion-insensitive, and closed-form solution, and also inherits virtues of the feature representations. Moreover, since the size of feature is often much smaller than the size of image, the FCF method can significantly reduce the storage requirement in recognition system. The comparative results on CMU-PIE and the FRGC2.0 database show that the proposed FCFs can achieve noteworthy performance improvement compared with their conventional counterpart.

AB - The correlation filters for pattern recognition, have been extensively studied in the areas of automatic target recognition(ATR) and biometrics. Whereas the conventional correlation filters perform directly on image pixels, in this paper, we propose a novel method, called "feature correlation filter (FCF)", by extending the concept of correlation filter to feature spaces. The FCF preserves the benefits of conventional correlation filters, i.e., shift-invariant, occlusion-insensitive, and closed-form solution, and also inherits virtues of the feature representations. Moreover, since the size of feature is often much smaller than the size of image, the FCF method can significantly reduce the storage requirement in recognition system. The comparative results on CMU-PIE and the FRGC2.0 database show that the proposed FCFs can achieve noteworthy performance improvement compared with their conventional counterpart.

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Feature correlation filter for face recognition

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Keywords

ASJC Scopus subject areas

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