A novel approach for pain intensity detection using facial feature deformations is presented.Thin Plate Spline gives features that are rotation, scaling and illumination invariant.To map the features to more discriminative space, distance metric learning method is proposed.The accuracy achieved by using the proposed approach is 96% for 16 level pain classification. 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.
['Neeru Rathee', 'Dinesh Ganotra']
A novel approach for pain intensity detection based on facial feature deformations