Real-time robust vision-based hand gesture recognition using stereo images

This paper presents a real-time and robust approach to recognize two types of gestures consisting of seven motional gestures and six finger spelling gestures. This approach utilizes stereo images captured by a stereo webcam to achieve robust recognition under realistic lighting conditions and in various backgrounds. It incorporates several existing computationally efficient techniques and introduces a rule-based approach to merge the information from a pair of stereo images leading to an improved hand detection compared to using single images. The results obtained indicate that high recognition rates under realistic conditions are obtained in real-time on PC platforms at the rate of 30 frames per second. It is shown that its outcome is comparable to two existing approaches while it is computationally more efficient than these approaches.