Advance morphological filtering, correlation and convolution method for gesture recognition

Hand gesture recognition system is employed to provide interface between computer and human using hand gesture. This paper presents a technique for human computer interface through common hand gesture that is efficient to commemorate 25 aspersion gestures from the American sign language hand alphabet. The prospect of this paper is to develop up an algorithm for hand gesture recognition with reasonable accuracy. This work uses a domain independent learning methodology to automatically stir low-level spatio-temporal descriptors for high-level cross recognition by Correlated variance programming. Feature extraction is the most important orientation for gesture recognition and is indeed important in terms of giving input to a classifier. In this work Canny edge detector algorithm is used to find edge of the segmented and morphological filtered image which yields boundary of hand gesture in the image then Correlated variance mean based programming applied for recognition of gesture. Experimental results very precisely indicate that the developed method outperforms the existing state of the art.