Two-Stream Aural-Visual Affect Analysis in the Wild

• URL: http://arxiv.org/abs/2002.03399v2
• Date: Tue, 3 Mar 2020 13:59:01 GMT
• Title: Two-Stream Aural-Visual Affect Analysis in the Wild
• Authors: Felix Kuhnke, Lars Rumberg, J\"orn Ostermann
• Abstract summary: We introduce our submission to the Affective Behavior Analysis in-the-wild (ABAW) 2020 competition. We propose a two-stream aural-visual analysis model to recognize affective behavior from videos. Our model achieves promising results on the challenging Aff-Wild2 database.
• Score: 2.578242050187029
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human affect recognition is an essential part of natural human-computer interaction. However, current methods are still in their infancy, especially for in-the-wild data. In this work, we introduce our submission to the Affective Behavior Analysis in-the-wild (ABAW) 2020 competition. We propose a two-stream aural-visual analysis model to recognize affective behavior from videos. Audio and image streams are first processed separately and fed into a convolutional neural network. Instead of applying recurrent architectures for temporal analysis we only use temporal convolutions. Furthermore, the model is given access to additional features extracted during face-alignment. At training time, we exploit correlations between different emotion representations to improve performance. Our model achieves promising results on the challenging Aff-Wild2 database.

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