Facial action unit detection is the task of detecting action units from a video of a face - for example, lip tightening and cheek raising.

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

# Self-Supervised Representation Learning From Videos for Facial Action Unit Detection

In this paper, we aim to learn discriminative representation for facial action unit (AU) detection from large amount of videos without manual annotations.

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# Multi-View Dynamic Facial Action Unit Detection

25 Apr 2017BCV-Uniandes/AUNets

We then move to the novel setup of the FERA 2017 Challenge, in which we propose a multi-view extension of our approach that operates by first predicting the viewpoint from which the video was taken, and then evaluating an ensemble of action unit detectors that were trained for that specific viewpoint.

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# Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment

Facial action unit (AU) detection and face alignment are two highly correlated tasks since facial landmarks can provide precise AU locations to facilitate the extraction of meaningful local features for AU detection.

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# Multitask Emotion Recognition with Incomplete Labels

We use the soft labels and the ground truth to train the student model.

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# J$\hat{\text{A}}$A-Net: Joint Facial Action Unit Detection and Face Alignment via Adaptive Attention

18 Mar 2020ZhiwenShao/PyTorch-JAANet

Moreover, to extract precise local features, we propose an adaptive attention learning module to refine the attention map of each AU adaptively.

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# Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition

In this work, we propose a semi-supervised approach for AU recognition utilizing a large number of web face images without AU labels and a relatively small face dataset with AU annotations inspired by the co-training methods.

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# Linear Disentangled Representation Learning for Facial Actions

11 Jan 2017eglxiang/icassp15_emotion

Limited annotated data available for the recognition of facial expression and action units embarrasses the training of deep networks, which can learn disentangled invariant features.

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# Automated Detection of Equine Facial Action Units

To automate parts of this process, we propose a Deep Learning-based method to detect EquiFACS units automatically from images.

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