Abstract
Gesture recognition technology has important significance in the field of human-computer interaction (HCI), the gesture recognition technology which is based on visual is sensitive to the impact of the experimental environment lighting, and so, the recognition result will produce a greater change; it makes this technology one of the most challenging topics. HOG feature which is successfully applied to pedestrian detection is operating on the local grid unit of image, so it can maintain a good invariance on geometric and optical deformation. In this paper, we extracted the gradient direction histogram (HOG) features of gestures, then, a Support Vector Machines is used to train these feature vectors, at testing time, a decision is taken using the previously learned SVMs, and compared the same gesture recognition rate in different light conditions. Experimental results show that the HOG feature extraction and multivariate SVM classification methods has a high recognition rate, and the system has a better robustness for the illumination.
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