  Human face analysis is an important task in computer vision. According to
cognitive-psychological studies, facial dynamics could provide crucial cues for
face analysis. The motion of a facial local region in facial expression is
related to the motion of other facial local regions. In this paper, a novel
deep learning approach, named facial dynamics interpreter network, has been
proposed to interpret the important relations between local dynamics for
estimating facial traits from expression sequence. The facial dynamics
interpreter network is designed to be able to encode a relational importance,
which is used for interpreting the relation between facial local dynamics and
estimating facial traits. By comparative experiments, the effectiveness of the
proposed method has been verified. The important relations between facial local
dynamics are investigated by the proposed facial dynamics interpreter network
in gender classification and age estimation. Moreover, experimental results
show that the proposed method outperforms the state-of-the-art methods in
gender classification and age estimation.
