PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Supervised relevance maps for increasing the distinctiveness of facial images
Edwin Hancock, Jing Wu and Michael Kawulok
Pattern Recognition Volume 44, Number 4, pp. 929-939, 2011. ISSN 0031-3203

Abstract

This paper shows how to improve holistic face analysis by assigning importance factors to different facial regions (termed as face relevance maps). We propose a novel supervised learning algorithm for generating face relevance maps to improve the discriminating capability of existing methods. We have successfully applied the developed technique to face identification based on the Eigenfaces and Fisherfaces methods, and also to gender classification based on principal geodesic analysis (PGA). We demonstrate how to iteratively learn the face relevance map using labelled data. Experimental results confirm the effectiveness of the developed approach.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:7305
Deposited By:Edwin Hancock
Deposited On:17 March 2011