Learning expression kernels for facial expression intensity estimation

Chia-Te Liao, Hui-Ju Chuang, Shang-Hong Lai

Abstract: 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.