UTILIZING THE BEZIER DESCRIPTORS FOR HAND GESTURE RECOGNITION

In this paper, a novel approach is proposed for hand gesture recognition by modelling the Bezier curves. We have adapted a three-step approach which begins with the skin-based hand segmentation method using the normal Gaussian distribution. It is followed by the feature extraction module where the hand centroid points are computed which are then fitted with Bezier curves. These fitted Bezier curve points are quantized and concatenated to build the Bezier descriptors. The extracted Bezier descriptors are finally classified by Hidden Markov Models (HMM) using Left-Right Banded (LRB) topology for hand gesture recognition. We have tested our proposed approach with different HMM models on the same hand centroid points (i.e., control points) fitted with Bezier curves and compare results. The experimental results show that our proposed approach is capable to detect hands, model the Bezier curves to build descriptors and classify the descriptors for hand gesture recognition in real situations which proves its applicability in the domain of Human Computer Interaction.