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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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Dual Learning for Facial Action Unit Detection Under Nonfull Annotation.

Shangfei Wang, Heyan Ding, Guozhu Peng

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    This study introduces a novel dual learning framework for facial action unit (AU) detection, reducing the need for extensive manual labeling by using expression-labeled data alone or with partial AU labels.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Facial Action Unit (AU) recognition typically demands fully labeled training images, a process that is labor-intensive and time-consuming.
    • Existing methods face challenges in AU detection due to the high cost of manual annotation.

    Purpose of the Study:

    • To propose a novel dual learning framework for facial Action Unit (AU) detection.
    • To address AU detection in semisupervised (partially AU-labeled) and weakly supervised (expression-labeled only) scenarios.
    • To leverage probabilistic duality and inherent data dependencies for improved AU detection and facial synthesis.

    Main Methods:

    • A dual learning framework integrating a classifier, image generator, and discriminator.
    • Utilizing the probabilistic duality between AU detection and face synthesis tasks.
    • Incorporating dependencies among AUs, between expressions and AUs, and between facial features and AUs.
    • Employing reconstruction losses and supervised loss in semisupervised settings.
    • Generating pseudo-paired data using domain knowledge in weakly supervised settings.

    Main Results:

    • The proposed method demonstrates superior performance in AU detection compared to existing approaches.
    • The framework achieves state-of-the-art results in facial synthesis tasks.
    • Experiments on three widely used datasets validate the effectiveness of the semisupervised and weakly supervised approaches.

    Conclusions:

    • The dual learning framework significantly alleviates the reliance on fully AU-labeled data for facial action unit recognition.
    • The method offers a robust solution for both semisupervised and weakly supervised AU detection.
    • The framework shows promising results for both AU detection and facial synthesis, highlighting its versatility.