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Leveraging recent advances in deep learning for audio-Visual emotion recognition

Pattern recognition letters, 2021-06, Vol.146, p.1-7 [Peer Reviewed Journal]

Attribution - NonCommercial ;ISSN: 0167-8655 ;EISSN: 1872-7344 ;DOI: 10.1016/j.patrec.2021.03.007

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  • Title:
    Leveraging recent advances in deep learning for audio-Visual emotion recognition
  • Author: Schoneveld, Liam ; Othmani, Alice ; Abdelkawy, Hazem
  • Subjects: Artificial Intelligence ; Computer Science
  • Is Part Of: Pattern recognition letters, 2021-06, Vol.146, p.1-7
  • Description: Emotional expressions are the behaviors that communicate our emotional state or attitude to others. They are expressed through verbal and non-verbal communication. Complex human behavior can be understood by studying physical features from multiple modalities; mainly facial, vocal and physical gestures. Recently, spontaneous multi-modal emotion recognition has been extensively studied for human behavior analysis. In this paper, we propose a new deep learning-based approach for audio-visual emotion recognition. Our approach leverages recent advances in deep learning like knowledge distillation and high-performing deep architectures. The deep feature representations of the audio and visual modalities are fused based on a model-level fusion strategy. A recurrent neural network is then used to capture the temporal dynamics. Our proposed approach substantially outperforms state-of-the-art approaches in predicting valence on the RECOLA dataset. Moreover, our proposed visual facial expression feature extraction network outperforms state-of-the-art results on the AffectNet and Google Facial Expression Comparison datasets.
  • Publisher: Elsevier
  • Language: English
  • Identifier: ISSN: 0167-8655
    EISSN: 1872-7344
    DOI: 10.1016/j.patrec.2021.03.007
  • Source: Hyper Article en Ligne (HAL) (Open Access)
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