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    This study introduces a novel four-stage fusion framework for automatic facial age estimation. The proposed system structures age estimation by incorporating gender recognition and grouping, significantly improving performance over existing methods.

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    Area of Science:

    • Computer Vision
    • Pattern Recognition
    • Machine Learning

    Background:

    • Automatic facial age estimation is crucial in computer vision and pattern recognition.
    • Existing methods primarily focus on feature extraction and model learning.
    • A gap exists in optimizing system structure for improved age estimation performance.

    Purpose of the Study:

    • To propose a novel four-stage fusion framework for facial age estimation.
    • To enhance age estimation accuracy by optimizing system structure under fixed feature and learning constraints.
    • To demonstrate the framework's effectiveness on benchmark datasets.

    Main Methods:

    • A four-stage fusion framework was developed: gender recognition, gender-specific age grouping, age estimation within groups, and fusion.
    • The framework was validated using MORPH-II, FG-NET, and CLAP2016 datasets.
    • Performance was evaluated under constrained conditions (fixed feature type, fixed learning method).

    Main Results:

    • The proposed framework significantly improved facial age estimation performance.
    • The four-stage fusion approach outperformed several state-of-the-art age estimation methods.
    • Gender recognition and grouping enhanced accuracy within the framework.

    Conclusions:

    • System structuring is a viable approach to improve facial age estimation.
    • The proposed four-stage fusion framework offers a significant advancement in automatic age estimation.
    • This method provides a robust and effective solution for facial age estimation challenges.