Representation and classification of facial expression in a modular computational model

Recognizing expressions is a key part of human social interaction; Processing of facial expression information is largely automatic in humans, but it is a non-trivial task for a computational system. The purpose of this work is to develop computational models capable of differentiating between a range of human facial expressions. Here we use two sets of images, namely: Angry and Neutral. Raw face images are examples of high dimensional data, so here we use some dimensionality reduction techniques: Principal Component Analysis and Curvilinear Component Analysis. We preprocess the images with a bank of Gabor filters, so that important features in the face images are identified. Subsequently the faces are classified using a Support Vector Machine. We also find the effect size of the pixels for the Angry and Neutral faces. We show that it is possible to differentiate faces with a neutral expression from those with an angry expression with high accuracy. Moreover we can achieve this with data that has been massively reduced in size: in the best case the original images are reduced to just 6 dimensions.