Light invariant real-time robust hand gesture recognition

Computer vision has spread over different domains to facilitate difficult operations. It works as the artificial eye for many industrial applications to observe elements, process, automation and to find defects. Vision-based systems can also be applied to normal human life operations but changing light conditions is a big problem for these systems. Hand gesture recognition can be embedded with many existing interactive applications/games to make interaction natural and easy but changing illumination and non-uniform backgrounds make it very difficult to perform operations with good image segmentation. If a vision based system is installed in public domain, different people are supposed to work on the application. 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. (C) 2017 Elsevier GmbH. All rights reserved.