Behaviour4All: in-the-wild Facial Behaviour Analysis Toolkit
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917787484553216 |
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| author | Kollias, Dimitrios Shao, Chunchang Kaloidas, Odysseus Patras, Ioannis |
| author_facet | Kollias, Dimitrios Shao, Chunchang Kaloidas, Odysseus Patras, Ioannis |
| contents | In this paper, we introduce Behavior4All, a comprehensive, open-source toolkit for in-the-wild facial behavior analysis, integrating Face Localization, Valence-Arousal Estimation, Basic Expression Recognition and Action Unit Detection, all within a single framework. Available in both CPU-only and GPU-accelerated versions, Behavior4All leverages 12 large-scale, in-the-wild datasets consisting of over 5 million images from diverse demographic groups. It introduces a novel framework that leverages distribution matching and label co-annotation to address tasks with non-overlapping annotations, encoding prior knowledge of their relatedness. In the largest study of its kind, Behavior4All outperforms both state-of-the-art and toolkits in overall performance as well as fairness across all databases and tasks. It also demonstrates superior generalizability on unseen databases and on compound expression recognition. Finally, Behavior4All is way times faster than other toolkits. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_17717 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Behaviour4All: in-the-wild Facial Behaviour Analysis Toolkit Kollias, Dimitrios Shao, Chunchang Kaloidas, Odysseus Patras, Ioannis Computer Vision and Pattern Recognition In this paper, we introduce Behavior4All, a comprehensive, open-source toolkit for in-the-wild facial behavior analysis, integrating Face Localization, Valence-Arousal Estimation, Basic Expression Recognition and Action Unit Detection, all within a single framework. Available in both CPU-only and GPU-accelerated versions, Behavior4All leverages 12 large-scale, in-the-wild datasets consisting of over 5 million images from diverse demographic groups. It introduces a novel framework that leverages distribution matching and label co-annotation to address tasks with non-overlapping annotations, encoding prior knowledge of their relatedness. In the largest study of its kind, Behavior4All outperforms both state-of-the-art and toolkits in overall performance as well as fairness across all databases and tasks. It also demonstrates superior generalizability on unseen databases and on compound expression recognition. Finally, Behavior4All is way times faster than other toolkits. |
| title | Behaviour4All: in-the-wild Facial Behaviour Analysis Toolkit |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.17717 |