Face recognition from multiple images per subject

For face recognition, we show that knowing that each subject corresponds to multiple face images can improve classification performance. For domains such as video surveillance, it is easy to deduce which group of images belong to the same subject; in domains such as family album identification, we lose group membership information but there is still a group of images for each subject. We define these two types of problems as multiple faces per subject. In this paper, we propose a Bipart framework to take advantage of this group information in the testing set as well as in the training set. From these two sources of information, two models are learned independently and combined to form a unified discriminative distance space. Furthermore, this framework is generalized to allow both subspace learning and distance metric learning methods to take advantage of this group information. Bipart is evaluated on the multiple faces per subject problem using several benchmark datasets, including video and static image data, subjects of various ages, various lighting conditions, and many facial expressions. Comparisons against state-of-the-art distance and subspace learning methods demonstrate much better performance when utilizing group information with the Bipart framework.