Wavelets Based Facial Expression Recognition Using a Bank of Neural Networks

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 Discrete Wavelet Transform (DWT) features to a bank of five parallel neural networks. Each neural network is trained to recognize a particular facial expression, so that it is most sensitive to that expression. Multi-classification is achieved by combining multiple neural networks performing binary classification using oneagainst-all approach. The outputs of all neural networks 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.