Gesture image sequence interpretation using the multi-PDM method and hidden Markov model

This paper introduces a multi-Principal-Distribution-Model (PDM) method and Hidden Markov Model (HMM) for gesture image sequence interpretation. To track the hand-shape, it uses the PDM model which is built by learning patterns of variability from a training set of correctly annotated images. For gesture recognition, we need to deal with a large variety of hand-shape. Therefore, we divide all the training hand shapes into a number of similar groups, with each group trained for an individual PDM shape model. Finally, we use the HMM to determine model transition among these PDM shape models. From the model transition sequence, it can identify the continuous gestures denoting one-digit or two-digit numbers.