Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace

By Dimitrios Kollias, Stefanos P. Zafeiriou. British Machine Vision Conference, 2019

413 citations recorded by the source corpus.

Affective computing has been largely limited in terms of available data resources. The need to collect and annotate diverse in-the-wild datasets has become apparent with the rise of deep learning models, as the default approach to address any computer vision task. Some in-the-wild databases have been recently proposed. However: i) their size is small, ii) they are not audiovisual, iii) only a small part is manually annotated, iv) they contain a small number of subjects, or v) they are not annotated for all main behavior tasks (valence-arousal estimation, action unit detection and basic expression classification). To address these, we substantially extend the largest available in-the-wild database (Aff-Wild) to study continuous emotions such as valence and arousal. Furthermore, we annotate parts of the database with basic expressions and action units. As a consequence, for the first time, this allows the joint study of all three types of behavior states. We call this database Aff-Wild2. We conduct extensive experiments with CNN and CNN-RNN architectures that use visual and audio modalities; these networks are trained on Aff-Wild2 and their performance is then evaluated on 10 publicly available emotion databases. We show that the networks achieve state-of-the-art performance for the emotion recognition tasks. Additionally, we adapt the ArcFace loss function in the emotion recognition context and use it for training two new networks on Aff-Wild2 and then re-train them in a variety of diverse expression recognition databases. The networks are shown to improve the existing state-of-the-art. The database, emotion recognition models and source code are available at this http URL.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/403c69991bcfd4d37e77da69d264023d25456d5c

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Citations in the corpus

Privileged Information for Modeling Affect In The Wild (2021, background): “Towards this direction large databases [20], [25]–[28] that simulate human emotions in the wild are necessary [29].”
Analysing Affective Behavior in the First ABAW 2020 Competition (2020, methodology): “The First Affective Behavior Analysis in-the-wild (ABAW) Competition relies on the Aff-Wild2 database [23], [19], [21].”