A novel approach for pain intensity detection based on facial feature deformations.

The pain intensity detection approach proposed in this paper is based on the fact that facial features get deformed during pain. To model facial feature deformations, Thin Plate Spline is adopted that separates rigid and non-rigid deformations very well. For efficient pain level detection, we have mapped the deformation parameters to higher discriminative space using Distance Metric Learning (DML) method. In DML, we seek a common distance metric such that the features belonging to the same pain intensity are pulled close to each other and the features belonging to the different pain intensity are pushed as far as possible. The assessment of the proposed approach is carried out on the popularly accepted UNBC-McMaster Shoulder Pain Expression Archive Database by using Support Vector Machine as a classifier. To prove the efficacy of the proposed approach, it is compared with state-of-the-art approaches mentioned in literature. [ABSTRACT FROM AUTHOR]