Performance Analysis of Accurate End Point Identification Method of Static Hand Gesture Recognition

Static Hand Gesture Recognition is an important area of research as it is used for developing a variety of useful applications in the domains like robotics, artificial intelligence, mobile games etc. Variety of useful methods is implemented to detect static hand gesture. In our previous study presented in [1] describes the implementation of Accurate End Point Identification (AEPI) Method for static hand gesture recognition. The AEPI method has been implemented to address the problems of varying background, luminance, blurring etc. Five different phases of AEPI method includes preprocessing, centroid detection, removal of unwanted objects, thinning and recognition which are already discussed in [1, 2]. In this paper, we present the result and performance analysis of AEPI method for all the possible input patterns of static hand gesture recognition.