Gesture localization and recognition using probabilistic visual learning

A generic approach for the extraction and recognition of gesture using raw grey-level images is presented. The probabilistic visual learning approach, a learning method recently proposed by B. Moghaddam and A. Pentland (1997), is used to create a set of compact statistical representations of gesture appearance on low dimensional eigenspaces. The same probabilistic modeling framework is used to extract and track gesture and to perform gesture recognition over long image sequences. Gesture extraction and tracking are based on maximum likelihood gesture detection in the input image. Recognition is performed by using the set of learned probabilistic appearance models as estimates of the emission probabilities of a continuous density hidden Markov model (CDHMM). Although the segmentation and CDHMM-based recognition use raw grey-level images, the method is fast, thanks to the data compression obtained by probabilistic visual learning. The approach is comprehensive and may be applied to other visual motion recognition tasks. It does not require application-tailored extraction of image features, the use of markers or gloves. A real-time implementation of the method on a standard PC-based vision system is under consideration.