Learning expression kernels for facial expression intensity estimation

Although many studies of facial expression analysis have been conducted, most previous works indeed focused on expression recognition. Different from previous works, this paper proposes a novel approach to learn the expression kernel for facial expression intensity estimation. The solution involves first aligning the optical flow to a neutral face to reduce inter-person variations in facial geometry, followed by solving an optimization problem with the ordinal ranking of expression intensities in temporal domain as constraints. Extensive experiments on the Cohn-Kanade database manifest that using the learned expression kernels leads to superior performance than the previous methods for facial expression intensity estimation.