Abstract | ||
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Facial action unit (AU) detection in the wild is a challenging problem, due to the unconstrained variability in facial appearances and the lack of accurate annotations. Most existing methods depend on either impractical labor-intensive labeling or inaccurate pseudo labels. In this paper, we propose an end-to-end unconstrained facial AU detection framework based on domain adaptation, which transfers accurate AU labels from a constrained source domain to an unconstrained target domain by exploiting labels of AU-related facial landmarks. Specifically, we map a source image with label and a target image without label into a latent feature domain by combining source landmark-related feature with target landmark-free feature. Due to the combination of source AU-related information and target AU-free information, the latent feature domain with transferred source label can be learned by maximizing the target-domain AU detection performance. Moreover, we introduce a novel landmark adversarial loss to disentangle the landmark-free feature from the landmark-related feature by treating the adversarial learning as a multi-player minimax game. Our framework can also be naturally extended for use with target-domain pseudo AU labels. Extensive experiments show that our method soundly outperforms lower-bounds and upper-bounds of the basic model, as well as state-of-the-art approaches on the challenging in-the-wild benchmarks. The code is available at
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Year | DOI | Venue |
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2022 | 10.1109/TAFFC.2021.3091331 | IEEE Transactions on Affective Computing |
Keywords | DocType | Volume |
Unconstrained facial AU detection,domain adaptation,landmark adversarial loss,feature disentanglement | Journal | 13 |
Issue | ISSN | Citations |
2 | 1949-3045 | 0 |
PageRank | References | Authors |
0.34 | 28 | 5 |
Name | Order | Citations | PageRank |
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Zhiwen Shao | 1 | 10 | 4.23 |
jianfei cai | 2 | 1804 | 147.18 |
Tat-jen Cham | 3 | 1006 | 88.85 |
Xuequan Lu | 4 | 64 | 17.63 |
Lizhuang Ma | 5 | 498 | 100.70 |