Multi-subregion-based probabilistic approach to pose-invariant face recognition

Current automatic facial recognition systems are not robust to changes in illumination, pose, facial expression, and occlusion. In this paper, in order to address the problem of pose change, we propose an algorithm based on a probabilistic approach to face recognition that takes into account the pose difference between probe and gallery images. By using a large facial image database, the CMU PIE database, which contains images of the same set of people taken from many different angles, we have developed a probabilistic model of how facial features change as the pose changes. This model enables us to make our face recognition system more robust to changes of pose in the probe image. The experimental results show that this approach achieves a better recognition rate than conventional face recognition methods over a much larger range of poses. For example, when the gallery contains only images of a frontal face and the probe image varies its pose orientation, the recognition rate shows less than a 10p difference until the probe pose begins to differ by more than 45°, whereas the recognition rate of a PCA-based method begins to drop at differences as small as 10°, and that of a representative commercial system begins to drop at 30°. © 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 37(8): 68–76, 2006; Published online in Wiley InterScience (). DOI 10.1002sscj.20437(This work was done while the first author was visiting the Robotics Institute of Carnegie Mellon)