FACIAL EXPRESSION RECOGNITION USING LOG-EUCLIDEAN STATISTICAL SHAPE MODELS

摘要

This paper presents a new method for facial expression modelling and recognition based on diffeomorphic image registration parameterised via stationary velocity fields in Log-Euclidean framework. The validation and comparison are done using different statistical shape models (SSM) built using the Point Distribution Model (PDM), velocity fields, and deformation fields. The obtained results show that the facial expression representation based on stationary velocity field can be successfully utilised in facial expression recognition, and this parameterisation produces higher recognition rate than the facial expression representation based on deformation fields.