An audiovisual and contextual approach for categorical and continuous emotion recognition in-the-wild

• URL: http://arxiv.org/abs/2107.03465v1
• Date: Wed, 7 Jul 2021 20:13:17 GMT
• Title: An audiovisual and contextual approach for categorical and continuous emotion recognition in-the-wild
• Authors: Panagiotis Antoniadis, Ioannis Pikoulis, Panagiotis P. Filntisis, Petros Maragos
• Abstract summary: We tackle the task of video-based audio-visual emotion recognition, within the premises of the 2nd Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW) Standard methodologies that rely solely on the extraction of facial features often fall short of accurate emotion prediction in cases where the aforementioned source of affective information is inaccessible due to head/body orientation, low resolution and poor illumination. We aspire to alleviate this problem by leveraging bodily as well as contextual features, as part of a broader emotion recognition framework.
• Score: 27.943550651941166
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work we tackle the task of video-based audio-visual emotion recognition, within the premises of the 2nd Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW). Standard methodologies that rely solely on the extraction of facial features often fall short of accurate emotion prediction in cases where the aforementioned source of affective information is inaccessible due to head/body orientation, low resolution and poor illumination. We aspire to alleviate this problem by leveraging bodily as well as contextual features, as part of a broader emotion recognition framework. A standard CNN-RNN cascade constitutes the backbone of our proposed model for sequence-to-sequence (seq2seq) learning. Apart from learning through the \textit{RGB} input modality, we construct an aural stream which operates on sequences of extracted mel-spectrograms. Our extensive experiments on the challenging and newly assembled Affect-in-the-wild-2 (Aff-Wild2) dataset verify the superiority of our methods over existing approaches, while by properly incorporating all of the aforementioned modules in a network ensemble, we manage to surpass the previous best published recognition scores, in the official validation set. All the code was implemented using PyTorch\footnote{\url{https://pytorch.org/}} and is publicly available\footnote{\url{https://github.com/PanosAntoniadis/NTUA-ABAW2021}}.

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