Facial Dynamics Interpreter Network: What are the Important Relations between Local Dynamics for Facial Trait Estimation?

TL;DR

This paper introduces a deep learning model called the facial dynamics interpreter network that analyzes the relationships between facial local movements to improve trait estimation like gender and age from expression sequences.

Contribution

The paper presents a novel neural network that encodes relational importance between facial local dynamics, enhancing facial trait estimation accuracy.

Findings

Outperforms state-of-the-art in gender classification

Outperforms state-of-the-art in age estimation

Effectively interprets relations between facial local dynamics

Abstract

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.