Abstract:
This paper introduces a gesture interpretation based on a
multi-Principal-Distribution-Model (PDM) and Hidden Markov Models
(HMMs). 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 theses PDM shape models. From the
model transition sequence, it can identify the continuous gestures
denoting one-digit or two-digit numbers.