FER by Modeling the Conditional Independence between the Spatial Cues and the Spatial Attention Distributions
Wan Ding, Dongyan Huang, Jingjun Liang, Jinlong Jiao, Zhiping Zhao · 2021
This paper presents a novel approach for FER. The spatial cues, for example the locations of face components such as eyes and mouth, play an important role to guide the spatial attentions for FER. Traditional approaches define the relations between the spatial cues and the spatial attention distributions based on linear models. However there also exists non-linear relations between them, in which case the spatial cues and the spatial attention distributions can be conditional independent. In this paper we model the conditional independence based on the state-of-the arts framework of the attention models for FER. We design the spatial cues as the hyper-parameters to affect the metric for spatial attention calculation. We exploit the Global-Attention (no spatial cues), Local-Attention (spatial cues affect the attention distributions) and Self-Attention (spatial cues as the hyper-parameters to affect the attention metric) as three different configurations. The experimental results show that the Self-attention achieves the best performances (68.5% on FER2013 Dataset and 49.8% on EmotiW2017 Dataset) which improves the accuracies by 2.8 % (on FER2013) and 1% (on EmotiW2017) compared with the Global-attention. The experimental results support the idea that non-linear modeling the relations between the spatial cues and the spatial attention distributions can improve the performances for FER.