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。

利用深度残差和卷积神经网络在野外进行面部情感估计

Facial Affect Estimation in the Wild Using Deep Residual and  Convolutional Networks

Deep Neural Networks (DNNs) have shown to outperform traditional methods in
various visual recognition tasks including Facial Expression Recognition (FER).
In spite of efforts made to improve the accuracy of FER systems using DNN,
existing methods still are not generalizable enough in practical applications.
This paper proposes a 3D Convolutional Neural Network method for FER in videos.
This new network architecture consists of 3D Inception-ResNet layers followed
by an LSTM unit that together extracts the spatial relations within facial
images as well as the temporal relations between different frames in the video.
Facial landmark points are also used as inputs to our network which emphasize
on the importance of facial components rather than the facial regions that may
not contribute significantly to generating facial expressions. Our proposed
method is evaluated using four publicly available databases in
subject-independent and cross-database tasks and outperforms state-of-the-art
methods.

本篇研究提出了在视频中进行情感识别的 3D 卷积神经网络方法，使用 3D Inception-ResNet 层及 LSTM 单元，从面部图像中提取空间关系和不同帧之间的时间关系，并利用面部关键点作为输入，该方法在四个公开数据库上的表现超过了现有最先进技术。