HAND GESTURE PHASE CLASSIFICATION USING MULTILAYER PERCEPTRON

Symbolic and Sign language is a most important technique for the non-verbal communication which uses the gestures i.e. the movement of the body part which carry some information to perform the desired goal. Gesture is a meaningful statements or information given by the human beings to accomplish the specific task. This paper presents an approach for Human-Computer Interaction (HCI) where we classify the different hand gesture phases using the movement of the hand as the input device. The main objective of this paper is to make a robust model to identify the different hand gesture phases using Multilayer Perceptron (MLP) with high accuracy.The MLP gives better classification accuracy as 83.95% with learning rate 0.6 and hidden layer 3.