Light invariant real-time robust hand gesture recognition

This paper demonstrates a light intensity invariant technique for hand gesture recognition which can be easily applied to other vision-based applications also. The technique has been tested on different people in different light conditions with the extreme change in intensity. This was done as one skin color looks different in changed light intensity and different skin colors may look same in changed light intensity. The orientation histogram was used to identify unique features of a hand gesture and it was compared using supervised ANN. The overall accuracy of 92.86% is achieved in extreme light intensity changing environments.