A novel spatially confined non-negative matrix factorization for face recognition

Hf Neo; Bj Teoh; Cl Ngo

A novel spatially confined non-negative matrix factorization for face recognition

Hf Neo, Bj Teoh, Cl Ngo

Department of Electrical and Electronic Engineering

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

8 Citations (Scopus)

Abstract

In this paper, a novel method for facial representation called Spatially Confined Non-Negative Matrix Factorization (SFNMF) is presented. SFNMF aims to extract more spatially confined, parts-based representation from the NMF based representation by merely removing non-prominent region, and focalize on the salient feature. SFNMF derived a significant set of basis which allows a non-subtractive representation of images and these bases manifest localized features. Experimental results are presented to compare SFNMF with NMF and Local NMF. Advantageous of SFNMF is demonstrated when SFNMF achieves highest verification rate among the other.

Original language	English
Title of host publication	Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005
Pages	502-505
Number of pages	4
Publication status	Published - 2005
Event	9th IAPR Conference on Machine Vision Applications, MVA 2005 - Tsukuba Science City, Japan Duration: 2005 May 16 → 2005 May 18

Publication series

Name	Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005

Conference

Conference	9th IAPR Conference on Machine Vision Applications, MVA 2005
Country/Territory	Japan
City	Tsukuba Science City
Period	05/5/16 → 05/5/18

All Science Journal Classification (ASJC) codes

Computer Vision and Pattern Recognition

Cite this

@inproceedings{1b681abca0f445a3a1ac627b1ba7ef19,

title = "A novel spatially confined non-negative matrix factorization for face recognition",

abstract = "In this paper, a novel method for facial representation called Spatially Confined Non-Negative Matrix Factorization (SFNMF) is presented. SFNMF aims to extract more spatially confined, parts-based representation from the NMF based representation by merely removing non-prominent region, and focalize on the salient feature. SFNMF derived a significant set of basis which allows a non-subtractive representation of images and these bases manifest localized features. Experimental results are presented to compare SFNMF with NMF and Local NMF. Advantageous of SFNMF is demonstrated when SFNMF achieves highest verification rate among the other.",

author = "Hf Neo and Bj Teoh and Cl Ngo",

year = "2005",

language = "English",

isbn = "4901122045",

series = "Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005",

pages = "502--505",

booktitle = "Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005",

note = "9th IAPR Conference on Machine Vision Applications, MVA 2005 ; Conference date: 16-05-2005 Through 18-05-2005",

}

Neo, H, Teoh, B & Ngo, C 2005, A novel spatially confined non-negative matrix factorization for face recognition. in Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005. Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005, pp. 502-505, 9th IAPR Conference on Machine Vision Applications, MVA 2005, Tsukuba Science City, Japan, 05/5/16.

A novel spatially confined non-negative matrix factorization for face recognition. / Neo, Hf; Teoh, Bj; Ngo, Cl.

Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005. 2005. p. 502-505 (Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005).

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

TY - GEN

T1 - A novel spatially confined non-negative matrix factorization for face recognition

AU - Neo, Hf

AU - Teoh, Bj

AU - Ngo, Cl

PY - 2005

Y1 - 2005

N2 - In this paper, a novel method for facial representation called Spatially Confined Non-Negative Matrix Factorization (SFNMF) is presented. SFNMF aims to extract more spatially confined, parts-based representation from the NMF based representation by merely removing non-prominent region, and focalize on the salient feature. SFNMF derived a significant set of basis which allows a non-subtractive representation of images and these bases manifest localized features. Experimental results are presented to compare SFNMF with NMF and Local NMF. Advantageous of SFNMF is demonstrated when SFNMF achieves highest verification rate among the other.

AB - In this paper, a novel method for facial representation called Spatially Confined Non-Negative Matrix Factorization (SFNMF) is presented. SFNMF aims to extract more spatially confined, parts-based representation from the NMF based representation by merely removing non-prominent region, and focalize on the salient feature. SFNMF derived a significant set of basis which allows a non-subtractive representation of images and these bases manifest localized features. Experimental results are presented to compare SFNMF with NMF and Local NMF. Advantageous of SFNMF is demonstrated when SFNMF achieves highest verification rate among the other.

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M3 - Conference contribution

AN - SCOPUS:84872544740

SN - 4901122045

SN - 9784901122047

T3 - Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005

SP - 502

EP - 505

BT - Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005

T2 - 9th IAPR Conference on Machine Vision Applications, MVA 2005

Y2 - 16 May 2005 through 18 May 2005

ER -

A novel spatially confined non-negative matrix factorization for face recognition

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

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