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Selective Transfer Machine for Personalized Facial Expression Analysis.

Wen-Sheng Chu, Fernando De la Torre, Jeffrey F Cohn

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |January 24, 2017
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    Summary
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    This study introduces a Selective Transfer Machine (STM) to improve automatic facial action unit (AU) detection. STM personalizes generic classifiers without new labels, outperforming existing methods on multiple benchmarks.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Affective Computing

    Background:

    • Automatic facial action unit (AU) detection is crucial for understanding human emotions but faces challenges due to individual differences in facial morphology and behavior.
    • Existing methods struggle to generalize classifiers to unseen subjects, limiting their real-world applicability.
    • The lack of sufficient person-specific training data hinders the development of personalized facial expression recognition models.

    Purpose of the Study:

    • To develop a novel method for personalizing generic facial action unit (AU) classifiers without requiring additional labeled data from the test subject.
    • To address the challenge of individual differences in facial morphology and behavior that impact AU detection accuracy.
    • To improve the generalization capabilities of facial expression recognition systems.

    Main Methods:

    • Proposes a transductive learning method named Selective Transfer Machine (STM).
    • STM simultaneously learns a classifier and re-weights training samples to reduce person-specific mismatches.
    • Evaluates STM on four benchmark datasets: CK+, GEMEP-FERA, RUFACS, and GFT.

    Main Results:

    • The Selective Transfer Machine (STM) significantly outperformed generic classifiers across all tested benchmarks.
    • STM demonstrated effectiveness in attenuating person-specific mismatches, leading to improved AU detection accuracy.
    • The proposed method shows promise for enhancing the personalization of facial expression recognition systems.

    Conclusions:

    • Selective Transfer Machine (STM) offers an effective solution for personalizing generic facial action unit (AU) classifiers without additional subject-specific labels.
    • The method successfully addresses the challenge of individual variability in facial analysis.
    • STM represents a significant advancement in cross-domain learning for facial expression recognition.