Comparative study for vision based and data based hand gesture recognition technique

Communication for mute people has always been very difficult. Mute people use hand gestures, also known as sign language to communicate with normal people. Hands gestures have their assigned meanings which may differ from person to person and hence cannot be understood by normal people. To overcome this difficulty many vision-based hand gesture recognition systems and data glove based hand gesture recognition systems have been proposed. This paper illustrates about two different techniques of vision-based hand gesture recognition and one data glove based technique. The vision — based techniques are static hand gesture recognition technique and real-time hand gesture recognition technique. In both the techniques, MATLAB software is used for processing input images and no dataset is used for decision making which makes this system more accurate than the existing system designs. In data glove based technique, the glove consists of five flex sensors. The change in resistance of flex sensor is used to recognize the hand gesture. All the three techniques are performed on 10 subjects and compared to find the most accurate technique. It was found that both the vision based techniques showed 100% accuracy in bright lighting condition with a white background while the data glove based technique showed an accuracy of 86%. This which clearly shows that mentioned vision based is more stable and reliable compared to the data glove based technique.