Facial Action Unit Detection via Adaptive Attention and Relation

• URL: http://arxiv.org/abs/2001.01168v2
• Date: Wed, 17 May 2023 03:18:31 GMT
• Title: Facial Action Unit Detection via Adaptive Attention and Relation
• Authors: Zhiwen Shao, Yong Zhou, Jianfei Cai, Hancheng Zhu, Rui Yao
• Abstract summary: We propose a novel adaptive attention and relation (AAR) framework for facial AU detection. Specifically, we propose an adaptive attention regression network to regress the global attention map of each AU under the constraint of attention predefinition. Considering the diversity and dynamics of AUs, we propose an adaptive-temporal graphal network to simultaneously reason independent pattern of each AU, the inter-dependencies among AUs, as well as the temporal dependencies.
• Score: 40.34933431651346
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Facial action unit (AU) detection is challenging due to the difficulty in capturing correlated information from subtle and dynamic AUs. Existing methods often resort to the localization of correlated regions of AUs, in which predefining local AU attentions by correlated facial landmarks often discards essential parts, or learning global attention maps often contains irrelevant areas. Furthermore, existing relational reasoning methods often employ common patterns for all AUs while ignoring the specific way of each AU. To tackle these limitations, we propose a novel adaptive attention and relation (AAR) framework for facial AU detection. Specifically, we propose an adaptive attention regression network to regress the global attention map of each AU under the constraint of attention predefinition and the guidance of AU detection, which is beneficial for capturing both specified dependencies by landmarks in strongly correlated regions and facial globally distributed dependencies in weakly correlated regions. Moreover, considering the diversity and dynamics of AUs, we propose an adaptive spatio-temporal graph convolutional network to simultaneously reason the independent pattern of each AU, the inter-dependencies among AUs, as well as the temporal dependencies. Extensive experiments show that our approach (i) achieves competitive performance on challenging benchmarks including BP4D, DISFA, and GFT in constrained scenarios and Aff-Wild2 in unconstrained scenarios, and (ii) can precisely learn the regional correlation distribution of each AU.

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