Personal identification from facial expression

Abstract

Motion-based face recognition is a new member to the family of biometrics. It studies personal characteristics concealed behind facial motions (e.g. facial expressions, speech) and uses the information for identity recognition. Research in this field is in its early stage and many questions remain unanswered.

This thesis contributes in two unexplored aspects of motion-based face recognition: the use of facial expression dynamics and cross-expression identification techniques. Two novel approaches are proposed respectively and tested through a series of experiments. The experimental results indicate that facial expression dynamics can be highly discriminative and cross-expression motion-based face recognition is possible.