Upper-body contour extraction and tracking using face and body shape variance information

We propose a fitting method using a model that integrates face and body shape variance information for upper-body contour extraction and tracking. Accurate body-contour extraction is necessary for various applications, such as pose estimation, gesture recognition, and so on. In this study, we regard it as the shape model fitting problem. A model including shape variance information can fit to the contour robustly even in the noisy case. AAMs are one of these models and can fit to a face successfully. It needs appearance information for effective fitting, but it can not be used in our case because appearance of upper-body easily changes by clothes. Instead of intensity image, proposed method uses edge image as appearance information. However, discrimination between a true contour edge of upper-body and other edges is difficult. To solve this problem, we integrate shape models of upper body and face. It is expected that this integrated model is more robust to edges in clutter background and various locations of the body than a body shape model using only body shape information. We conduct experiments and confirm improvement in accuracy by integration of face and body variance information.