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New improved feature extraction methods for real-time high performance image sequence recognition

This paper describes new feature extraction methods which can be used very effectively in combination with statistical methods for image sequence recognition. Although these feature extraction methods can be used for a wide variety of image sequence processing applications, the target application presented in this paper is gesture recognition. The novel feature extraction methods have been integrated into an HMM-based gesture recognition system and led to substantial improvements for this system. It turned out that the new features are not only able to describe the gesture characteristics much better than the old features, but additionally they also led to a dramatic reduction in dimensionality of the feature vector used for representing each frame of the image sequence. This resulted in the fact that it was possible to use the novel features in combination with a new architecture for statistical image sequence recognition. The result of this investigation is a high performance gesture recognition system with significantly improved recognition rates and real-time capabilities.