论文标题
深度卷积神经网络基于野外的面部表达识别
Deep Convolutional Neural Network Based Facial Expression Recognition in the Wild
论文作者
论文摘要
本文介绍了提出的方法，所使用的数据以及我们参与2020年情感行为行为分析的ChallengetRack 2（Expr挑战赛）的结果。在2020年竞争中。在本次竞争中，我们已经在给定的数据集中使用了拟议的深卷积神经网络（CNN）模型来执行自动面部表达识别（AFER）。我们提出的模型的准确度为50.77％，而验证集的F1得分为29.16％。
This paper describes the proposed methodology, data used and the results of our participation in the ChallengeTrack 2 (Expr Challenge Track) of the Affective Behavior Analysis in-the-wild (ABAW) Competition 2020. In this competition, we have used a proposed deep convolutional neural network (CNN) model to perform automatic facial expression recognition (AFER) on the given dataset. Our proposed model has achieved an accuracy of 50.77% and an F1 score of 29.16% on the validation set.
