Tracking and Recognition of Face Sequences

In this work, we address the issue of encoding and recognition of face sequences that arise from continuous head movement. We detect and track a moving head before segmenting face images from on-line camera inputs. We measure temporal changes in the pattern vectors of eigenface projections of successive image frames of a face sequence and introduce the concept of "temporal signature" of a face class. We exploit two di#erent supervised learning algorithms with feedforward and partially recurrent neural networks to learn possible temporal signatures. We discuss our experimental results and draw conclusions. 1 Problem Statement Face recognition is important for the interpretation of facial expressions in applications such as intelligent man-machine interface and communication, intelligent visual surveillance, teleconference and real-time animation from live motion images. There have been many computer models proposed for machine-based recognition of face images [21, 17, 4]. Among them, t..