A Study on Optimal Face Ratio for Recognition Using Part-Based Feature Extractor

Author

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

Author_Institution

Fac. of Inf. Sci. & Technol., Multimedia Univ., Melaka

fYear

2007

fDate

16-18 Dec. 2007

Firstpage

735

Lastpage

741

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.

Keywords

face recognition; feature extraction; face recognition; feature extractor; nonnegative matrix factorization; optimal face ratio; Biometrics; Data mining; Face recognition; Feature extraction; Humans; Information science; Internet; Multimedia systems; Real time systems; Surveillance; face ratio; non-negative matrix factorization; part-based feature extractor;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal-Image Technologies and Internet-Based System, 2007. SITIS '07. Third International IEEE Conference on

Conference_Location

Shanghai

Print_ISBN

978-0-7695-3122-9

Type

conf

DOI

10.1109/SITIS.2007.52

Filename

4618846