Facial expression recognition and its degree estimation using fuzzy clustering algorithm on Gabor-PCA features

Abstract

Although many system s exist for autom a tic classification of faces according to their em otional expression, these system s do not explicitly estim ate the strength of given expressions. In th is thes is a n algor ithm capable of e s tim ating th e degre e to which a face expres ses a given emotion is des c ribed and empirically evaluated. The system first align s and norm a l i zes an inpu t face im age. It then applies a f ilter bank of Gabor wavelets and re duces the data’s dim e nsionality via princip a l com ponents analysis (PCA). Finally, an unsupervised F u zzy-C-Mean (FCM) clustering algorithm is employed to induce a set of cluster m e mberships, which are then m a pped to sub-groups (degrees) of a facial expression (i.e. Less Happy (LH), Moderately Happy (MH), and Very Happy (VH)). This unsupervised m e thod is used to determ ine the best pair of Principle Com ponents and the centroieds of the clusters for latter clas sification. To test the hypothesis facial express i on from the Carnegie M e lo n University (CMU) facial exp r ess i on data base is used. The test re sults on four basic em oti on’s (Happy, Surprised, Angry and Sad) degree estim ation reflect the hypothesis. The accuracy is m easured em pirically which shows it is poss i b l e to es tim a te the degree of facial ex pression using the FC M algorithm.