Facial Action Unit Recognition With Multi-models Ensembling

Facial Action Unit Recognition With Multi-models Ensembling

The Affective Behavior Analysis in-the-wild (ABAW) 2022 Competition gives Affective Computing a large promotion. In this paper, we present our method of AU challenge in this Competition. We use improved IResnet100 as backbone. Then we train AU dataset in Aff-Wild2 on three pertained models pretraine...

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Veröffentlicht in:arXiv.org 2022-03
Hauptverfasser: Jiang, Wenqiang, Wu, Yannan, Qiao, Fengsheng, Meng, Liyu, Deng, Yuanyuan, Liu, Chuanhe
Format: Artikel
Sprache:eng
Schlagworte:
Affective computing
Competition
Datasets
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Zusammenfassung:The Affective Behavior Analysis in-the-wild (ABAW) 2022 Competition gives Affective Computing a large promotion. In this paper, we present our method of AU challenge in this Competition. We use improved IResnet100 as backbone. Then we train AU dataset in Aff-Wild2 on three pertained models pretrained by our private au and expression dataset, and Glint360K respectively. Finally, we ensemble the results of our models. We achieved F1 score (macro) 0.731 on AU validation set.
ISSN:2331-8422