Improving Haar Cascade Classifiers Through the Synthesis of New Training Examples

Authors

Abstract

A Genetic Programming approach for the improvement of the performance of classifier systems through the synthesis of new training instances is explored. Genetic Programming is used to exploit shortcomings of classifiers systems and generate misclassified instances. The proposed approach performs multiple parallel evolutionary runs to generate a large number of potentially misclassified samples. A supervisor module determines which of the generated images have been misclassified and which should be added to the training set. New classifiers are trained based on the original training set augmented by the selected evolved instances. The results attained while using face detection classifiers are presented and discussed. Overall they indicate that significant improvements are attained when using multiple evolutionary runs.

Keywords

Genetic programming, Performance measures, Machine learning

Conference

Soule, T. and Moore, J.H. editors. 2012. Genetic and Evolutionary Computation Conference, GECCO ’12, Philadelphia, PA, USA, July 7-11, 2012, Companion Material Proceedings. GECCO (Companion) (2012), July 2012