论文标题
使用多任务学习卷积网络在野外的面部影响识别
Facial Affect Recognition in the Wild Using Multi-Task Learning Convolutional Network
论文作者
论文摘要
本文提出了一种基于神经网络的方法多任务情感网络（MTANET），该方法提交了FG2020中情感行为分析的野外行为分析。此方法是一个多任务网络，基于SE-Resnet模块。通过利用多任务学习，该网络可以同时估计并识别三个量化的情感模型：价值和唤醒，动作单位和七个基本情绪。 MTANET的价值和唤醒的一致性相关系数（CCC）速率为0.28和0.34，F1得分为0.427，AUS检测和分类情绪分类为0.427和0.32。
This paper presents a neural network based method Multi-Task Affect Net(MTANet) submitted to the Affective Behavior Analysis in-the-Wild Challenge in FG2020. This method is a multi-task network and based on SE-ResNet modules. By utilizing multi-task learning, this network can estimate and recognize three quantified affective models: valence and arousal, action units, and seven basic emotions simultaneously. MTANet achieve Concordance Correlation Coefficient(CCC) rates of 0.28 and 0.34 for valence and arousal, F1-score of 0.427 and 0.32 for AUs detection and categorical emotion classification.
