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Distilling Ordinal Relation and Dark Knowledge for Facial Age Estimation.

Qilu Zhao, Junyu Dong, Hui Yu

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    This study introduces a two-teacher knowledge distillation method for facial age estimation. It effectively transfers ordinal and classification knowledge into a compact model, improving performance on resource-limited devices.

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    Area of Science:

    • Computer Vision
    • Machine Learning

    Background:

    • Facial age estimation is challenging due to non-stationary aging patterns.
    • Exact age values are less reliable than age order for estimation.
    • Existing methods may require significant computational resources.

    Purpose of the Study:

    • To develop an efficient knowledge distillation approach for facial age estimation.
    • To improve the performance of compact models for age estimation.
    • To enable facial age estimation on mobile and embedded devices.

    Main Methods:

    • A novel two-teacher knowledge distillation framework is proposed.
    • Teacher 1: A ranking method capturing ordinal age relations by recovering ordered sequences.
    • Teacher 2: A classification model mirroring the student architecture.

    Main Results:

    • The proposed method effectively distills ordinal and classification knowledge.
    • The compact student model shows superior performance compared to state-of-the-art methods.
    • The approach facilitates deployment on resource-constrained platforms.

    Conclusions:

    • Knowledge distillation with ordinal and classification teachers enhances facial age estimation.
    • The method provides a computationally efficient solution for age estimation.
    • This approach is suitable for real-world applications on mobile and embedded systems.