Gesture recognition using the multi-PDM method and hidden Markov model

摘要

This paper introduces a multi-Principal-Distribution-Model (PDM) method and Hidden Markov Model (HMM) for gesture recognition. 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. However, it can only fit the hand examples that are similar to shapes of the corresponding training set. For gesture recognition, we need to deal with a large variety of hand-shapes. 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, the system can identify the continuous gestures representing one-digit or two-digit numbers.