Group Level Audio-Video Emotion Recognition Using Hybrid Networks
Chuanhe Liu, Wenqiang Jiang, Minghao Wang, Tianhao Tang
Abstract
This paper presents a hybrid network for audio-video group Emo-tion Recognition. The proposed architecture includes audio stream,facial emotion stream, environmental object statistics stream (EOS)and video stream. We adopted this method at the 8th EmotionRecognition in the Wild Challenge (EmotiW2020). According to thefeedback of our submissions, the best result achieved 76.85% in theVideo level Group AFfect (VGAF) Test Database, 26.89% higherthan the baseline. Such improvements prove that our method isstate-of-the-art.
Topics & Concepts
Computer scienceEmotion recognitionBaseline (sea)Speech recognitionObject (grammar)ArchitectureArtificial intelligenceMultimediaVisual artsGeologyArtOceanographySpeech and Audio ProcessingMusic and Audio ProcessingVideo Surveillance and Tracking Methods