Vision-Based Hand Gesture Recognition Using PCA+Gabor Filters and SVM

In this paper we present a novel method for hand gesture recognition based on Gabor filters and support vector machine (SVM). Gabor filters are first convolved with images to acquire desirable hand gesture features. The principal components analysis (PCA) method is then used to reduce the dimensionality of the feature space. With the reduced Gabor features, SVM is trained and exploited to perform the hand gesture recognition tasks. To confirm the robustness of the proposed method, a dataset with large posed-angle (>45 deg.) of hand gestures is created. The experiment result shows that the recognition rate of 95.2% can be achieved when SVM is used. A real-time video system for hand gesture recognition is also presented with a processing rate of 0.2 s for every frame. This result proves the efficiency and superiority of the proposed Gabor-SVM method.