People Identification with Limited Labels in Privacy-Protected Video

Abstract

People identification is an essential task for video content analysis in a surveillance system. To construct a good classifier requires a large amount of training data, which may not be obtained in some scenario. In this paper, we propose an approach to augment insufficient training data by labeling identical video images that have removed people's identities by masking faces. We show user study results that human subjects can perform reasonably well in labeling pairwise constraints from face obscured images. We also present a new discriminative learning algorithm WPKLR to handle uncertainties in pairwise constraints. The effectiveness of the proposed approach is demonstrated using video captured in a nursing home environment. The experiments show that the WPKLR approach can obtain a high accuracy of people identification using limited labeled data and noisy pairwise constraints, and meanwhile minimize the risk of exposing people's identities.