Two viewpoints based real-time recognition for hand gestures

It is extremely challenging to accomplish excellent accuracy for gesture recognition using an approach where complexity in computation time for recognition is less. This study compares accuracy in hand gesture recognition of a single viewpoint set-up with proposed two viewpoint set-up for different classification techniques. The efficacy of the presented approach is verified practically with various image processing, feature extraction and classification techniques. Two camera system make geometry learning and three-dimensional (3D) view feasible compared to a single camera system. Geometrical features from additional viewpoint contribute to 3D view estimation of the hand gesture. It also improves the classification accuracy. Experimental results demonstrate that the proposed method show escalation in recognition rate compared to the single-camera system, and also has great performance using simple classifiers like the nearest neighbour and decision tree. Classification within 1 s is considered as real-time in this study.