论文标题
视频中面部表达分析的合奏方法
An Ensemble Approach for Facial Expression Analysis in Video
论文作者
论文摘要
人类的情绪识别有助于人类计算机相互作用的发展。理解现实世界中人类情绪的机器将在未来为生活做出重大贡献。本文将在野外介绍情感行为分析（ABAW3）2022挑战。本文着重于解决价估计和动作单元检测的问题。对于价值估计，我们进行了两个阶段：创建来自多模型和时间学习的新特征，以预测价值。首先，我们制作新功能；使用常规网络（Regnet）功能合并门控复发单元（GRU）和变压器，该功能是从图像中提取的。下一步是GRU结合了当地的注意，以预测价值。一致性相关系数（CCC）用于评估模型。
Human emotions recognization contributes to the development of human-computer interaction. The machines understanding human emotions in the real world will significantly contribute to life in the future. This paper will introduce the Affective Behavior Analysis in-the-wild (ABAW3) 2022 challenge. The paper focuses on solving the problem of the valence-arousal estimation and action unit detection. For valence-arousal estimation, we conducted two stages: creating new features from multimodel and temporal learning to predict valence-arousal. First, we make new features; the Gated Recurrent Unit (GRU) and Transformer are combined using a Regular Networks (RegNet) feature, which is extracted from the image. The next step is the GRU combined with Local Attention to predict valence-arousal. The Concordance Correlation Coefficient (CCC) was used to evaluate the model.
