Sorted locally confined non-negative matrix factorization in face verification

Andrew B.J. Teoh, H. F. Neo, David C.L. Ngo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In this paper, we propose a face recognition technique based on modification of Non-Negative Matrix Factorization (NMF) technique, which known as Sorted Locally Confined NMF (SLC-NMF). SLC-NMF used NMF to find non negative basis images, subset of them were selected according to a discriminant factor and then processed through a series of image processing operation; to yield a set of ideal locally confined salient feature basis images. SLC-NMF illustrates perfectly local salient feature region which effectively realize "recognition by parts" paradigm for face recognition. The best performance is attained by SLC-NMF compare to the PCA, NMF and local NMF, in FERET Face Database.

Original languageEnglish
Title of host publication2005 International Conference on Communications, Circuits and Systems - Proceedings
Pages820-824
Number of pages5
DOIs
Publication statusPublished - 2005 Dec 1
Event2005 International Conference on Communications, Circuits and Systems - Hong Kong, China
Duration: 2005 May 272005 May 30

Publication series

Name2005 International Conference on Communications, Circuits and Systems - Proceedings
Volume2

Other

Other2005 International Conference on Communications, Circuits and Systems
CountryChina
CityHong Kong
Period05/5/2705/5/30

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All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Teoh, A. B. J., Neo, H. F., & Ngo, D. C. L. (2005). Sorted locally confined non-negative matrix factorization in face verification. In 2005 International Conference on Communications, Circuits and Systems - Proceedings (pp. 820-824). [BMSP-02.P(#06_01_02)] (2005 International Conference on Communications, Circuits and Systems - Proceedings; Vol. 2). https://doi.org/10.1109/ICCCAS.2005.1495236