Real-Time Hand Posture Recognition Using Haar-Like and Topological Feature

Abstract

A new method based on Haar-like and topological feature is proposed for hand posture recognition. Initially, the region of the hand is detected by a statistical method based on Haar-like features and color segmentation technique. With this method, a group of hand posture regions can be detected in real time with high recognition accuracy. Then, the topology is applied on the detected regions so as to classify the different postures. Applying this method to human-robot interaction, experimental results show that our method achieves satisfactory performance.