Texture analysis based facial expression recognition using a bank of Bayesian classifiers

A human face does not only identify an individual but also communicates useful information about a person´s emotional state. No wonder automatic face expression recognition has become an area of immense interest within the computer science, psychology, medicine and human-computer interaction research communities. Various feature extraction techniques based on statistical to geometrical data have been used for recognition of expressions from static images as well as real time videos. In this paper we present a method for automatic recognition of facial expressions from face images by providing Texture features to a bank of five parallel Bayesian classifiers. Each Bayesian classifier is trained to recognize a particular facial expression, so that it is most sensitive to that expression. Multi-classification is achieved by combining multiple Bayesian classifiers each performing binary classification using one-against-all approach. The outputs of all classifiers are combined using a maximum function. The classification efficiency is tested on static images from the publicly available JAFFE database. The experiments using the proposed method demonstrate promising results.