The Taub Faculty of Computer Science Events and Talks

We introduce a new approach for identifying individuals based on their motion patterns in interactive scenarios. We formalize the identification process in the context of a sequential message exchange session between the subject and the system. The subject is modeled with a probabilistic generative model inspired by the Human Information Processing (HIP) paradigm. At each stage, the system presents a visual stimulus (a cue) to the subject and records their motion response. The cue is selected so as to maximize the mutual information of the expected response and the subject's identity. Once recorded, the response is used to update the a posteriori probability over possible subjects' identities. To the best of our knowledge, this is the first time person identification is addressed in an interactive setting. We tested our approach using a novel dataset consisting of 4,476 recordings of 22 test subjects responding to 15 cues. The dataset will be made available for public use. We also report results on a publicly available MSRC12 dataset. Our experiments show that the proposed identification approach is effective and reaches a predefined confidence level using fewer iterations than the random selection strategy.