Facial Affect Recognition in the Wild Using Multi-Task Learning Convolutional Network

• URL: http://arxiv.org/abs/2002.00606v1
• Date: Mon, 3 Feb 2020 09:02:26 GMT
• Title: Facial Affect Recognition in the Wild Using Multi-Task Learning Convolutional Network
• Authors: Zihang Zhang, Jianping Gu
• Abstract summary: This paper presents a neural network based method submitted to the Affective Behavior Analysis in-the-Wild Challenge in FG 2020. By utilizing multi-task learning, this network can estimate and recognize three quantified affective models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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