Multi-output Laplacian dynamic ordinal regression for facial expression recognition and intensity estimation

Automated facial expression recognition has received increased attention over the past two decades. Existing works in the field usually do not encode either the temporal evolution or the intensity of the observed facial displays. They also fail to jointly model multidimensional (multi-class) continuous facial behaviour data; binary classifiers one for each target basic-emotion class are used instead. In this paper, intrinsic topology of multidimensional continuous facial affect data is first modeled by an ordinal manifold. This topology is then incorporated into the Hidden Conditional Ordinal Random Field (H-CORF) framework for dynamic ordinal regression by constraining H-CORF parameters to lie on the ordinal manifold. The resulting model attains simultaneous dynamic recognition and intensity estimation of facial expressions of multiple emotions. To the best of our knowledge, the proposed method is the first one to achieve this on both deliberate as well as spontaneous facial affect data.