Multiple-Classifiers Based Hand Gesture Recognition

Simin Li, Zihan Ni, Nong Sang

Abstract: Gesture recognition technology is important in the field of human-computer interaction (HCI), the gesture recognition technology which is based on visual is sensitive to the impact of the environment. We proposed a multiple-classifiers based gesture recognition algorithm that recognizes ten kinds of gesture. The algorithm gets the size and the direction of the gesture by hand tracking algorithm which has the ability of segmentation and can give us the rough outline of the tracked hand. Based on this information, we rotate the image and get the upright image of the hand gesture. Then we extract the HOG feature of the upright hand image, and use multiple classifiers to classify the gesture. The algorithm has a better recognition rate whether the background color is similar to the skin or complex.