Learning to Associate Faces across Views in Vector Space of Similarities to Prototypes
Shaogang Gong, Eng-Jon Ong, Stephen James McKenna · 1998
We present a method for learning appearance models that can be used to recognise and track both 3D head pose and identities of novel subjects with continuous head movement across the view-sphere. We describe an automatic face data acquisition system based on a magnetic sensor and a calibrated camera. The system enabled us to obtain systematically a database of face images with labelled 3D poses across a view-sphere of \\Sigma90 ffi yaw and \\Sigma30 ffi tilt at intervals of 10 ffi . The database was used to learn appearance models of unseen faces based on similarity measures to prototype faces. The method is computationally efficient and enables real-time performance with ease. 1 Introduction To be able to recognise faces of moving people not only requires the ability to label novel face images with known identities, but also needs detecting and tracking of faces over time [1]. We refer to this as the task of associating faces. We adopt the view such a task can be better achieved...