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Proceedings Paper

Face recognition with non-negative matrix factorization
Author(s): Menaka Rajapakse; Lonce Wyse
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Paper Abstract

A face can conceptually be represented as a collection of sparsely distributed parts: eyes, nose, mouth etc.We use Non-negative Matrix Factorization (NMF) to yield sparse representation of localized features to represent distributed parts over a human face. This paper explores the potential of NMF for face recognition and the possibilities for gender-based features in face reconstruction. Further, we compare the results of NMF with other common face recognition methods.

Paper Details

Date Published: 23 June 2003
PDF: 10 pages
Proc. SPIE 5150, Visual Communications and Image Processing 2003, (23 June 2003); doi: 10.1117/12.502365
Show Author Affiliations
Menaka Rajapakse, Institute for Infocomm Research (Singapore)
Lonce Wyse, Institute for Infocomm Research (Singapore)


Published in SPIE Proceedings Vol. 5150:
Visual Communications and Image Processing 2003
Touradj Ebrahimi; Thomas Sikora, Editor(s)

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