Identification from ceiling: unconstrained person identification for tabletops using multiview learning

This paper presents novel unconstrained person identification for tabletop systems using a ceiling-mounted depth camera that overlooks a table. Recent state-of-the-art ubicomp, computer-vision, and CSCW studies have tried to recognize a user's activities and actions on a table using a ceiling-mounted device that overlooks the table. However, conventional unconstrained person identification methods such as face identification cannot be used for providing personalized services in such settings. In this study, we focus on a user's soft biometrics that can be captured from the ceiling such as the shoulder length, shape of the head, and posture of the back to achieve unconstrained person identification by using a ceiling-mounted depth camera. We achieve robust person identification by combining the soft biometrics within a framework of multiview learning. Multiview learning allows us to deal effectively with data consisting of features from multiple sources with different data distributions, i.e., multiple soft biometrics in our case. To the best of our knowledge, this is the first study that investigates the feasibility of person identification for tabletop users by a ceiling-mounted depth camera.