Human hand gestures as a natural way of communication with computers is becoming an emerging field of research due to its various applications like sign language recognition, human computer interaction, gaming, virtual reality etc. Hand gesture recognition under 2D environment has some limitations as the information about the other dimension (z-axis) is missed out. So, hand gesture recognition under 3D environment is becoming a growing field of research. In this paper, we have proposed a novel technique to detect hand gesture with forward and backward movement towards and away from the camera respectively. Our technique is based on stereo-vision and we have used a disparity map-based centroid movement and changing of its intensity as feature to recognize the gesture with conditional random field (CRF) as classifier. Stereo calibration and rectification is done to get the rectified images and correspondence gives the depth map. We tested our proposed method for Arabic numerals (0-9) and it worked efficiently with an average recognition rate of 88%.
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