Most algorithms [3, 7, 10,11] proposed so far for pose invariant face recognition need several images of each subject. We propose an approach that can recognize faces in a variety of poses even if a gallery database includes images of only one pose per person. The method works as follows. When a probe image is given, the face region in the image is detected and its landmarks, such as the eyes, are localized. The resulting probe face region is registered with that of the face in the gallery. The face region is divided into a set of small subregions, and each subregion is compared with the corresponding subregion of the face in the gallery. To compare the two, a similarity value for each subregion, defined by the sum of squared difference (SSD) after image normalization (so effectively the same as normalized correlation), is computed after fmer alignment is done in order to compensate for the potential error in registration and the local deformation due to pose and other variations. The total similarity value between the probe face and the gallery face is then obtained by combining the similarity values of all subregions. The key idea of OUT approach is that in combining those similarity values of subregions we take into account how the similarity value of each subregion, and thus its utility, changes as the pose of the face changes. We have developed a probabilistic model of that change by using a large set of training images from the CMU PIE database [4], which consists of face images of a set of people from many viewing angles. In a face recognition task of different poses, it was shown that our algorithm outperformed a baseline algorithm (PCA) and a commercial product for face recognition.
Kanade and Yamada REF developed a probabilistic model of how face appearance changes with viewpoint, in which the facial images are separated into a set of independent patches.
Multi-subregion based probabilistic approach toward pose-invariant face recognition
Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No.03EX694)
Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No.03EX694)
Computer Science