Classifying facial attributes using a 2-D Gabor wavelet representation and discriminant analysis

A method for automatically classifying facial images is proposed. Faces are represented using elastic graphs labelled with with 2D Gabor wavelet features. The system is trained from examples to classify faces on the basis of high-level attributes, such as sex, "race", and expression, using linear discriminant analysis (LDA). Use of the Gabor representation relaxes the requirement for precise normalization of the face: approximate registration of a facial graph is sufficient. LDA allows simple and rapid training from examples, as well as straightforward interpretation of the role of the input features for classification. The algorithm is tested on three different facial image datasets, one of which was acquired under relatively uncontrolled conditions, on tasks of sex, "race" and expression classification. Results of these tests are presented. The discriminant vectors may be interpreted in terms of the saliency of the input features for the different classification tasks, which we portray visually with feature saliency maps for node position as well as filter spatial frequency and orientation.