A Study on optimal face ratio for recognition using part-based feature extractor

Han Foon Neo; Chuan Chin Teo; Andrew Beng Jin Teoh

doi:10.1109/SITIS.2007.52

A Study on optimal face ratio for recognition using part-based feature extractor

Han Foon Neo, Chuan Chin Teo, Andrew Beng Jin Teoh

Department of Electrical and Electronic Engineering

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

5 Citations (Scopus)

Abstract

This paper aims to investigate the optimal face ratio for recognition. Face data are normalized to several ratios, which are 25% 50% (equivalent to right and left face), and 75% of the full-face. The advantages of using different face ratios are these face data reduce the amount of computational power and storage requirements significantly. For fair comparison, various part-based linear subspace feature extractors, namely Non-negative matrix factorization (NMF), Local NMF (LNMF) and Spatially Confined NMF (SFNMF) are used to estimate the optimal face ratio. Our results show that 75% faces are good enough to produce demonstrably recognition accuracy.

Original language	English
Title of host publication	Proceedings - International Conference on Signal Image Technologies and Internet Based Systems, SITIS 2007
Pages	735-741
Number of pages	7
DOIs	https://doi.org/10.1109/SITIS.2007.52
Publication status	Published - 2007
Event	3rd IEEE International Conference on Signal Image Technologies and Internet Based Systems, SITIS'07 - Jiangong Jinjiang, Shanghai, China Duration: 2007 Dec 16 → 2007 Dec 18

Publication series

Name	Proceedings - International Conference on Signal Image Technologies and Internet Based Systems, SITIS 2007

Other

Other	3rd IEEE International Conference on Signal Image Technologies and Internet Based Systems, SITIS'07
Country/Territory	China
City	Jiangong Jinjiang, Shanghai
Period	07/12/16 → 07/12/18

All Science Journal Classification (ASJC) codes

Computer Graphics and Computer-Aided Design
Computer Networks and Communications
Signal Processing

Access to Document

10.1109/SITIS.2007.52

Cite this

Neo, H. F., Teo, C. C., & Teoh, A. B. J. (2007). A Study on optimal face ratio for recognition using part-based feature extractor. In Proceedings - International Conference on Signal Image Technologies and Internet Based Systems, SITIS 2007 (pp. 735-741). [4618846] (Proceedings - International Conference on Signal Image Technologies and Internet Based Systems, SITIS 2007). https://doi.org/10.1109/SITIS.2007.52

Neo, Han Foon ; Teo, Chuan Chin ; Teoh, Andrew Beng Jin. / A Study on optimal face ratio for recognition using part-based feature extractor. Proceedings - International Conference on Signal Image Technologies and Internet Based Systems, SITIS 2007. 2007. pp. 735-741 (Proceedings - International Conference on Signal Image Technologies and Internet Based Systems, SITIS 2007).

@inproceedings{1c68991d73554ec0842bf852d5ea3861,

title = "A Study on optimal face ratio for recognition using part-based feature extractor",

abstract = "This paper aims to investigate the optimal face ratio for recognition. Face data are normalized to several ratios, which are 25% 50% (equivalent to right and left face), and 75% of the full-face. The advantages of using different face ratios are these face data reduce the amount of computational power and storage requirements significantly. For fair comparison, various part-based linear subspace feature extractors, namely Non-negative matrix factorization (NMF), Local NMF (LNMF) and Spatially Confined NMF (SFNMF) are used to estimate the optimal face ratio. Our results show that 75% faces are good enough to produce demonstrably recognition accuracy.",

author = "Neo, {Han Foon} and Teo, {Chuan Chin} and Teoh, {Andrew Beng Jin}",

year = "2007",

doi = "10.1109/SITIS.2007.52",

language = "English",

isbn = "9780769531229",

series = "Proceedings - International Conference on Signal Image Technologies and Internet Based Systems, SITIS 2007",

pages = "735--741",

booktitle = "Proceedings - International Conference on Signal Image Technologies and Internet Based Systems, SITIS 2007",

note = "3rd IEEE International Conference on Signal Image Technologies and Internet Based Systems, SITIS'07 ; Conference date: 16-12-2007 Through 18-12-2007",

}

Neo, HF, Teo, CC & Teoh, ABJ 2007, A Study on optimal face ratio for recognition using part-based feature extractor. in Proceedings - International Conference on Signal Image Technologies and Internet Based Systems, SITIS 2007., 4618846, Proceedings - International Conference on Signal Image Technologies and Internet Based Systems, SITIS 2007, pp. 735-741, 3rd IEEE International Conference on Signal Image Technologies and Internet Based Systems, SITIS'07, Jiangong Jinjiang, Shanghai, China, 07/12/16. https://doi.org/10.1109/SITIS.2007.52

A Study on optimal face ratio for recognition using part-based feature extractor. / Neo, Han Foon; Teo, Chuan Chin; Teoh, Andrew Beng Jin.
Proceedings - International Conference on Signal Image Technologies and Internet Based Systems, SITIS 2007. 2007. p. 735-741 4618846 (Proceedings - International Conference on Signal Image Technologies and Internet Based Systems, SITIS 2007).

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

TY - GEN

T1 - A Study on optimal face ratio for recognition using part-based feature extractor

AU - Neo, Han Foon

AU - Teo, Chuan Chin

AU - Teoh, Andrew Beng Jin

PY - 2007

Y1 - 2007

N2 - This paper aims to investigate the optimal face ratio for recognition. Face data are normalized to several ratios, which are 25% 50% (equivalent to right and left face), and 75% of the full-face. The advantages of using different face ratios are these face data reduce the amount of computational power and storage requirements significantly. For fair comparison, various part-based linear subspace feature extractors, namely Non-negative matrix factorization (NMF), Local NMF (LNMF) and Spatially Confined NMF (SFNMF) are used to estimate the optimal face ratio. Our results show that 75% faces are good enough to produce demonstrably recognition accuracy.

AB - This paper aims to investigate the optimal face ratio for recognition. Face data are normalized to several ratios, which are 25% 50% (equivalent to right and left face), and 75% of the full-face. The advantages of using different face ratios are these face data reduce the amount of computational power and storage requirements significantly. For fair comparison, various part-based linear subspace feature extractors, namely Non-negative matrix factorization (NMF), Local NMF (LNMF) and Spatially Confined NMF (SFNMF) are used to estimate the optimal face ratio. Our results show that 75% faces are good enough to produce demonstrably recognition accuracy.

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M3 - Conference contribution

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T3 - Proceedings - International Conference on Signal Image Technologies and Internet Based Systems, SITIS 2007

SP - 735

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BT - Proceedings - International Conference on Signal Image Technologies and Internet Based Systems, SITIS 2007

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Neo HF, Teo CC, Teoh ABJ. A Study on optimal face ratio for recognition using part-based feature extractor. In Proceedings - International Conference on Signal Image Technologies and Internet Based Systems, SITIS 2007. 2007. p. 735-741. 4618846. (Proceedings - International Conference on Signal Image Technologies and Internet Based Systems, SITIS 2007). doi: 10.1109/SITIS.2007.52

A Study on optimal face ratio for recognition using part-based feature extractor

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