Automated affective computing in the wild is a challenging task in the field
of computer vision. This paper presents three neural network-based methods
proposed for the task of facial affect estimation submitted to the First
Affect-in-the-Wild challenge. These methods are based on Inception-ResNet
modules redesigned specifically for the task of facial affect estimation. These
methods are: Shallow Inception-ResNet, Deep Inception-ResNet, and
Inception-ResNet with LSTMs. These networks extract facial features in
different scales and simultaneously estimate both the valence and arousal in
each frame. Root Mean Square Error (RMSE) rates of 0.4 and 0.3 are achieved for
the valence and arousal respectively with corresponding Concordance Correlation
Coefficient (CCC) rates of 0.04 and 0.29 using Deep Inception-ResNet method.

本文研究自然环境下的情感计算，提出了三种基于神经网络的方法，结合 Inception-ResNet 模块用于面部表情预测，分别为浅层 Inception-ResNet、深层 Inception-ResNet 和带有 LSTMs 的 Inception-ResNet，采用 Deep Inception-ResNet 方法可以实现 valence 和 arousal 误差率分别达到 0.4 和 0.3，相应的 CCC 率分别为 0.04 和 0.29。