Comparative Learning of Hand Gesture Recognition Method

Gesture Recognition means detection and identification of gestures originates from body motion but generally originate from face or hand. Present focuses in the field include feeling recognition from the face and hand gesture recognition with the improvement of ever-present computing, current user interaction approaches with keyboard, and mouse and pen are not enough. Due to the restriction of these devices the useable command set is also narrow. Direct use of hands as an input device is an smart technique for providing natural Human Computer Interaction which has evolved from text-based interfaces through, graphical-based interfaces, multimedia-supported interfaces. Human duplication for his surrounding atmosphere makes him interfere in every details of this massive environment, hear impaired people are gesturing with each other for delivering a particular message, this method of communication also attracts human duplication concentration to express it on human-computer interaction. Gesture recognition enables humans to interface with the machine and correlate naturally without any mechanical devices. The applications of gesture recognition are various, ranging from sign language through medical analysis to necessary reality. A comparative study included in this paper with focusing on different segmentation, features extraction and identification tools, research advantages and drawbacks are provided as well.