In the data mining course of my master degree on Applied AI, one of the techniques used for reducing dimensionality in preparing the raw data to be mined is Principal Components Analysis (PCA) I’ve searched the web for applications of PCA and I’ve found out this interesting application:
Michael J. Lyons, Julien Budynek, Shigeru Akamatsu
ATR Human Information Processings Labs
2-2 Hikari-dai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan
A method for the automatic classification of images of facial expression is proposed. The method uses a 2D Gabor wavelet representation and a linear discriminant classification scheme. Use of this representation relaxes the requirement for full normalization of the face. The algorithm is tested on two distinct databases of the fundamental facial expressions. We present results on the performance of the system, provide a visual interpretation of the discriminant vectors, and discuss the relevance of the findings to psychological studies of facial expression.