Abstract
Hand Gesture recognition system is a process involving classifying the given gesture of the hand portion. This paper presents a technique for the recognition of hand gesture from the 11 different static gestures taken from NUS hand posture dataset. Hand gesture detection in the complex background is seen as a challenging task. The purpose of this paper is to study and develop a method for the efficient detection and classification of hand gestures in the complex background. Skin similarity measure is used to detect the hand in complex background hand gesture image. The whole of the image is divided into two classes one is hand, and other is background. Subsequently, shape and texture features are extracted from the gestures which form the basis of recognition of the hand gesture.
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