Pulse-coupled neural network feature generation model for Arabic sign language recognition

Many feature generation methods have been developed for object recognition. Some of these methods succeeded in achieving invariance against object translation, rotation and scaling but faced problems of the bright background effect and non-uniform light on the quality of the generated features. This problem has hindered recognition systems from working in a free environment. This paper proposes a new method to enhance the feature quality based on pulse-coupled neural network. An adaptive model that defines continuity factor is proposed as a weight factor of the current pulse in signature generation process. The proposed new method has been employed in a hybrid feature extraction model that is followed by a classifier and was applied and tested in Arabic sign language static hand posture recognition; the superiority of the new method is shown.