Discovering person identity via large-scale observations

Abstract

Person identification is a well studied problem in the last two decades. In a typical automated person identification scenario, the system always contains the prior knowledge, either person-based model or reference mugshot, of the person-of-interest. However, the challenge of automated person identification would increase by multiple folds if the prior information is not available. In today’s world, rich and large quantity of information are easily attainable through the Internet or closed-loop surveillance network. This provides us an opportunity to employ an automated approach to perform person identification with minimum prior knowledge, presume that there are sufficient amount of observations. In this paper, we propose a dominant set based person identification framework to learn the identity of a person through largescale observations, where each observation contains instances from various modality. Through experiments on two challenging face datasets we show the potential of the proposed approach. We also explore the conditions required to obtain satisfy performance and discuss the potential future research directions.