LAUSR

ABSTRACT Facial action unit intensity detection is the key concern of researchers as it gives a much broader information about facial expressions of the individual. In the proposed work, an attempt is done to detect the intensity of the facial action unit by combining geometric deformations and appearance deformations of facial features. Thin plate spline is adopted for extracting geometric deformations, and Gabor filters are adopted for extracting appearance deformations. To combine both the description mentioned above, a metric learning method is used to combine the descriptors in such a way that complimentary information is extracted from them. Moreover, it also maps the features to higher discriminative space. The features are applied to support vector machine for the facial action unit intensity detection. The proposed approach is evaluated on the popularly accepted database: DISFA database and UNBC shoulder pain database. The results are compared with the state-of-the-art approaches to prove the efficacy of the suggested approach.