Real-time dynamic hand gesture recognition using hidden Markov models

The goal of interaction between human and computer is to find a way to treat it like human-human interaction. Gestures play an important role in human's daily life in order to transfer data and human emotions. The gestures are results of part of body movement in which hand movement is the most widely used one that is known as dynamic hand gesture. So it is very important to follow and recognize hand motion to provide multi-purpose use. In this paper, we propose a system that recognizes hand gestures from continuous hand motion for English numbers from 0 to 9 in real-time, based on Hidden Markov Models (HMMs). There are two kinds of gestures, key gestures and link gestures. The link gestures are used to separate the key gestures from other hand motion trajectories (gesture path) that are called spotting. This type of spotting is a heuristic-based method that identifies start and end points of the key gestures. Then gesture path between these two points are given to HMMs for classification. Experimental results show that the proposed system can successfully recognize the key gestures with recognition rate of 93.84%and work in complex situations very well.