A spatiotemporal approach for vision-based hand gesture recognition using Hough transform and neural network

Recognition of hand gesture is a crucial task as far as accuracy of recognition and real-time performance is concerned. The recognition accuracy of hand gesture systems used in applications from virtual reality to sign language recognition depends not only on the features extracted but also on the soft computing techniques used to recognize the gesture. This requires recognition system, with robust features which are unaffected by rotation, scaling, translation, variation in illumination and view of the gesture within the image. Accordingly, the research work presented in this paper aims to recognize the dynamic hand gestures using Hough transform-based spatiotemporal feature extraction for preprocessing and artificial neural network for identification. The system is implemented and tested using benchmark Cambridge hand gesture database and Sebastien dynamic hand posture database. As many as 32 features comprising of 19 Fourier descriptors, 3 geometrical features and 10 temporal features are obtained on each gesture sequence and are given as input to NN. The experimental results revealed that this approach is able to recognize dynamic hand gestures with an average recognition rate (RR) higher than 94% on Cambridge database which is better than that reported by earlier researchers while 98% RR on Sebastien database. The noteworthy feature of the proposed approach is that it relieves from the burden of foreground detection, generally required for other video processing systems.