Improving face detection

Authors:
Penousal Machado;João Correia;Juan Romero
Affiliations:
CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal;CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal;Faculty of Computer Science, University of A Coruña, Coruña, Spain
Venue:
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Year:
2012

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Abstract

A novel Genetic Programming approach for the improvement of the performance of classifier systems through the synthesis of new training instances is presented. The approach relies on the ability of the Genetic Programming engine to identify and exploit shortcomings of classifier systems, and generate instances that are misclassified by them. The addition of these instances to the training set has the potential to improve classifier's performance. The experimental results attained with face detection classifiers are presented and discussed. Overall they indicate the success of the approach.