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.

Physical activity consists complex behavior, typically structured in bouts which can consist of one continuous movement (e.g. exercise) or many sporadic movements (e.g. household chores). Each bout can be represented as a block of feature vectors corresponding to the same activity type. This paper introduces a general distance metric technique to use this block representation to first predict activity type, and then uses the predicted activity to estimate energy expenditure within a novel framework. This distance metric, dubbed Bipart, learns block-level information from both training and test sets, combining both to form a projection space which materializes block-level constraints. Thus, Bipart provides a space which can improve the bout classification performance of all classifiers. We also propose an energy expenditure estimation framework which leverages activity classification in order to improve estimates. Comprehensive experiments on waist-mounted accelerometer data, comparing Bipart against many similar methods as well as other classifiers, demonstrate the superior activity recognition of Bipart, especially in low-information experimental settings.