Abstract:
Hand Gesture Recognition play vital role for developing human computer
interaction and sign language recognition. Sign language recognition is used for deaf
and dump people. Existing system based on vision based static hand gesture
recognition. It performed static hand gesture recognition. In existing system,
Contour tracking algorithm is used for feature extraction and radial basis functional
neural network (RBFNN) is used for classification. RBFNN provides good
classification accuracy. Existing system is limited to static hand gesture recognition.
Proposed work performs Dynamic Hand Gesture Recognition using clustering based
technique. Clustering based technique provides good classification accuracy. Fourier
Descriptor method is used for feature extraction in the proposed method. This
method will reduce time complexity and it will improve accuracy.