Multiple-angle Hand Gesture Recognition by Fusing SVM Classifiers

摘要

This article presents a robust visual system that allows effective recognition of multiple-angle hand gestures in finger guessing games. Three support vector machine classifiers were trained for the construction of the hand gesture recognition system. The classified outputs were fused by proposed plans to improve system performance. Our experimental results show that the system presented by this article can effectively recognize hand gestures, at over 93%, of different angles, sizes, and different skin colors.