Hand Shape Estimation Using Sequence of Multi-Ocular Images Based on Transition Network

This paper presents a method of hand posture estimation from silhouette images taken by multiple cameras. For each image, we extract a feature vector from the silhouette contour of the hand. We construct an eigenspace by the feature vectors extracted from the hands of various postures. The feature vectors projected into the eigenspace are registered as models. The matching criterion of each images is defined as the distance to the model. The hand shape is estimated by retrieving the registered model well-matching to the input. For effective matching, we define a shape complexity for each image to see how well the shape feature is represented. For a set of input images taken by multiple cameras at each time, the total matching criterion is evaluated by combining the matching criteria of the set of images using the shape complexities. For rapid processing, we limit the matching candidate by using the constraint on the shape change. The possible shape transition is represented by a transition network. Because the network is hard to build, we apply offline learning, where nodes and links are automatically created by showing examples of hand shape sequences. We show experiments of building the transition networks and the performance of matching using the network.