Facial Dynamics Interpreter Network: What are the Important Relations between Local Dynamics for Facial Trait Estimation?
This work addresses facial analysis for computer vision applications, but it appears incremental as it builds on existing deep learning approaches for interpreting facial dynamics.
The paper tackles the problem of estimating facial traits like gender and age from expression sequences by interpreting important relations between local facial dynamics, and the proposed method outperforms state-of-the-art methods in gender classification and age estimation.
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.