PERFORMANCE EVALUATION OF NEURAL NETWORK BASED HAND GESTURE RECOGINITION

With the development of information technology in our Society one can expect that computer systems to a larger extent will be embedded into our daily life. These environments lead to the new types of human-computer interaction (HCI). The use of hand gestures provides an attractive alternative to cumbersome interface devices for human-computer interaction (HCI). The existing HCI techniques may become a bottleneck in the effective utilization of the available information flow. Gestures are expressive, meaningful body motions. Interpretation of human gestures such as hand movements or facial expressions, using mathematical algorithms is done using gesture recognition. Gesture recognition is also important for developing alternative human-computer interaction modalities. This research will have tested the proposed algorithm over 100 sign images of ASL. The simulation will show that the true match rate is increased from 77.7% to 84% while the false match rate is decreased from 8.33 % to 7.4%.