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Supervised relevance maps for increasing the distinctiveness of facial images

Kawulok, Michal, Wu, Jing and Hancock, Edwin R. 2011. Supervised relevance maps for increasing the distinctiveness of facial images. Pattern Recognition 44 (4) , pp. 929-939. 10.1016/j.patcog.2010.10.010

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords: Face recognition, Gender classification, Feature extraction, Biometric
Publisher: Elsevier
ISSN: 0031-3203
Date of Acceptance: 8 October 2010
Last Modified: 04 Jun 2017 09:03
URI: http://orca.cf.ac.uk/id/eprint/89860

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