Host: ISCIE, SICE, JSME, JSPE, JSASS, SCEJ
Co-host: Technically Cosponsored by 48 Societies and Institutes
In recent years, security camera systems have been installed in various public facilities. It is needed to implement more intelligent processes as tracking persons for image sequences of security camera systems. In this paper, we propose a face tracking and recognition method based on a Bayesian framework. We assume that an observed space is three-dimensional space, and we estimate a 3D position of a person. We use facial 3D shape, movement, and texture models for face tracking and recognition. An omnidirectional image sensors are used to acquire image sequences of a walking person because the sensors has wide views and are profitable for object tracking. Our system generates 3D positional hypotheses based on the facial movement model and these positional hypotheses are projected onto an image plane. Image features are extracted around projected hypotheses and the system distinguishes face using the image features. As results of evaluation experiments, we show that our proposed method is effective or face tracking and the tracking accuracy is improved in proportional to the number of cameras.
