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    This study introduces a novel framework for facial action unit (AU) detection, improving accuracy by modeling feature and AU dependencies. The joint patch and multi-label learning (JPML) approach enhances performance on benchmark datasets.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Human-Computer Interaction

    Background:

    • Current action unit (AU) detection methods often overlook dependencies between facial features and AUs, limiting their accuracy.
    • Existing approaches typically employ one-versus-all classifiers, failing to capture the complex interplay of facial muscle movements.

    Purpose of the Study:

    • To introduce a joint patch and multi-label learning (JPML) framework for modeling structured dependencies in facial action unit detection.
    • To develop a method that simultaneously considers feature importance, AU co-occurrence, and their interrelations for improved detection accuracy.
    • To extend the framework for holistic facial expression recognition using compact patch representations.

    Main Methods:

    • Developed a joint patch and multi-label learning (JPML) framework integrating group sparsity for patch identification and a multi-label classifier.
    • Derived and incorporated two AU relation models (positive correlation and negative competition) based on statistical analysis of over 350,000 video frames.
    • Evaluated the JPML framework on CK+, BP4D, and GFT datasets using within- and cross-dataset experimental setups.

    Main Results:

    • The JPML framework achieved the highest averaged F1 scores in four out of five experiments compared to baseline and single-component methods.
    • Demonstrated superior performance over methods using only patch learning or only multi-label learning.
    • Showcased the framework's ability to be extended for holistic expression recognition, yielding more compact representations.

    Conclusions:

    • The proposed JPML framework effectively models the structured joint dependencies between facial features and action units, outperforming existing methods.
    • The derived AU relations provide valuable constraints for multi-label classification in AU detection.
    • JPML offers a promising approach for both detailed AU detection and holistic facial expression recognition.