Brightness Factor Matching for Gesture Recognition System Using Scaled Normalization

The rich information found in the human gestures makes it possible to be used for another language which is called the sign language, this kind of intuitive interface can be used with human-made machines/devices as well, we herein going to introduce a new gesture recognition system based on image blocking and the gestures are recognized using our suggested brightness factor matching algorithm, we have applied two different feature extraction techniques, the first one based on features extracted from edge information and the other one based on a new technique for centre of mass normalization based on block scaling instead of coordinates shifting; we have achieved 83.3% recognition accuracy in first technique with significant and satisfactory recognition time of 1.5 seconds per gesture, and 96.6 % recognition accuracy with recognition time less than one second by eliminating the use of edge detector which consumes time, this paper focuses on appearance based gestures.