摘要：When automatically analyzing images of human faces, either for recognition in biometry applications or facial expression analysis in human machine interaction, one has to cope with challenges caused by different head pose, illumination and expression. In this article we propose a new stereo based method for effectively solving the pose problem through 3D face detection and normalization. The proposed method applies a model-based matching and is especially intended for the study of facial features and the description of their dynamic changes in image sequences under the assumption of non-cooperative persons. In our work, we are currently implementing a new application to observe and analyze single faces of post-operative patients. In the proposed method, face detection is based on color driven clustering of 3D points derived from stereo. A mesh model is matched with the post-processed face cluster using a variant of the Iterative Closest Point algorithm (ICP). Pose is derived from correspondence. Then, pose and model information is used for the synthesis of the face normalization. Results show, stereo and color are powerful cues for finding the face and its pose under a wide range of poses, illuminations and expressions (PIE). Head orientation may vary in out of plane rotations up to ±45°.
关键词：Image and Video Processing; ICP-Matching; Computer Vision; 3D Face Detection; Normalization
