PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Projective non-negative matrix factorization with applications to facial image processing
Zhirong Yang, Zhijian Yuan and Jorma Laaksonen
International Journal on Pattern Recognition and Artificial Intelligence Volume 21, Number 8, pp. 1353-1362, 2007.

Abstract

We propose a new variant of Non-negative Matrix Factorization (NMF), including its model and two optimization rules. Our method is based on positively constrained projections and is related to the conventional SVD or PCA decomposition. The new model can potentially be applied to image compression and feature extraction problems. Of the latter, we consider processing of facial images, where each image consists of several parts and for each part the observations with different lighting mainly distribute along a straight line through the origin. No regularization terms are required in the objective functions and both suggested optimization rules can easily be implemented by matrix manipulations. The experiments show that the derived base vectors are spatially more localized than those of NMF. In turn, the better part-based representations improve the recognition rate of semantic classes such as the gender or existence of mustache in the facial images.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
User Modelling for Computer Human Interaction
Learning/Statistics & Optimisation
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:3639
Deposited By:Jorma Laaksonen
Deposited On:14 February 2008