Automatic recognition of hand gestures with differential evolution

Abstract

Automatic recognition of hand gestures is a crucial step in facing human-computer interaction. Differential Evolution is used to perform automatic classification of hand gestures in a thirteen-class database. Performance of the resulting best individual is computed in terms of error rate on the testing set, and is compared against those of other ten classification techniques well known in literature. Results show the effectiveness and the efficiency of the approach in solving the classification task. Furthermore, the implemented tool allows to extract the most significant parameters for differentiating the collected gestures. © 2008 Springer-Verlag Berlin Heidelberg.