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    This study introduces a new method for facial expression recognition (FER) using class-incremental learning. The Relationship-Guided Knowledge Transfer (RGKT) method effectively recognizes both basic and compound expressions while preventing model forgetting.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Facial Expression Recognition (FER) is crucial for human-computer interaction.
    • Recognizing compound facial expressions incrementally presents a significant challenge due to the stability-plasticity dilemma.
    • Existing methods struggle to effectively learn new expression categories without forgetting previously learned ones.

    Purpose of the Study:

    • To develop a novel method for comprehensive facial expression recognition (FER) within the class-incremental learning paradigm.
    • To address the stability-plasticity dilemma in incremental learning for FER.
    • To improve the model's ability to learn new compound expressions while retaining knowledge of basic expressions.

    Main Methods:

    • Proposed a novel Relationship-Guided Knowledge Transfer (RGKT) method for class-incremental FER.
    • Developed a multi-region feature learning (MFL) module for extracting fine-grained expression features.
    • Introduced a basic expression-oriented knowledge transfer (BET) module to enhance plasticity for new classes.
    • Implemented a compound expression-oriented knowledge transfer (CET) module to improve stability by preventing forgetting of old classes.

    Main Results:

    • The proposed RGKT method demonstrated superior performance compared to state-of-the-art methods on three facial expression databases.
    • The MFL module effectively captured subtle differences in facial expressions.
    • The BET and CET modules successfully balanced model plasticity and stability during incremental learning.

    Conclusions:

    • The RGKT method offers an effective solution for comprehensive facial expression recognition in a class-incremental setting.
    • The approach successfully mitigates the stability-plasticity dilemma, enabling robust learning of new expression categories.
    • This research advances the field of FER by providing a more stable and adaptable incremental learning framework.