[Summary]
This paper presents an approach to recognize hand gestures in video. Both the hand shape and hand position information are involved into the recognition process. They call their method as "Predictive EigenTracker". In addition, this method also allows users to choose a gesture vocabulary so as to maximize recognition accuracy.
Basically, they employ a Particle Filtering (condensation) predictive framework to track the hand first. The dynamic model used in this tracker is a second-order Markov chain with noise. The tracker is initialized by detecting the hand skin color.
After tracking the hand position in video, a shape-trajectories eigenspace is modeled by principle components analysis. And then Mahalanobis distance between gestures are computed to help users to select a proper gesture set with highest accuracy.
To demonstrate the performance, they showed an application of their tracker and recognizer, controlling an audio player with hand gestures. This application ended up with a 100% accuracy.
[Discussion]
I think this system will not be robust. The only result shown in this paper is a set of very simple gestures with totally different color as background, and the performer are in a black shirt, which makes the tracking problem very easy. Moreover, this system cannot work on a larger set of gestures. This paper only showed us a set with 8 gestures.
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