Entropy-based iterative face classification

Authors:
Marios Kyperountas;Anastasios Tefas;Ioannis Pitas
Affiliations:
Department of Informatics, Aristotle University of Thessaloniki, Greece;Department of Informatics, Aristotle University of Thessaloniki, Greece;Department of Informatics, Aristotle University of Thessaloniki, Greece and Informatics and Telematics Institute, Centre for Research and Technology Hellas, Greece
Venue:
BioID'11 Proceedings of the COST 2101 European conference on Biometrics and ID management
Year:
2011

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Abstract

This paper presents a novel methodology whose task is to deal with the face classification problem. This algorithm uses discriminant analysis to project the face classes and a clustering algorithm to partition the projected face data, thus forming a set of discriminant clusters. Then, an iterative process creates subsets, whose cardinality is defined by an entropy-based measure, that contain the most useful clusters. The best match to the test face is found when one final face class is retained. The standard UMIST and XM2VTS databases have been utilized to evaluate the performance of the proposed algorithm. Results show that it provides a good solution to the face classification problem.