Facial Expression Recognition Using Statistical Shape Analysis
Jieyu Zhao · Journal of Ningbo University · 2009
Facial expression interpretation and recognition is a key issue in visual communication and human-machine interaction.This paper proposes a face expression analysis method based on the statistical shape analysis in order to achieve an accurate model of facial expression.Facial feature contours are regarded as configurations of a stochastic process governed by a statistical shape model.After computing the mean shape and aligning all shapes resulting from the training set by means of a Procrustes analysis,shape variations are estimated using the Principal Component Analysis.Then,the set of facial expression shapes are modeled using a multivariate Gaussian distribution and maximum likelihood estimation is applied to the data from the models.The conditional probability and the prophetic probability of the shape data are estimated.Finally,an experiment to classify some basic facial expression is set up,demonstrating that the proposed method works with adequate efficiency and can recognize a given number of facial expressions.