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    This study introduces SphereFace-R, an improved hyperspherical face recognition method that enhances training stability. SphereFace-R achieves competitive performance while overcoming the limitations of earlier models.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Deep face recognition faces challenges in open-set protocols, requiring distinct intra-class and inter-class feature distances.
    • Hyperspherical face recognition, focusing on angular margins, has emerged as a key research area.
    • SphereFace introduced large inter-class angular margins but suffered from training instability.

    Purpose of the Study:

    • To address the training instability of SphereFace in hyperspherical face recognition.
    • To propose a unified framework for understanding large angular margins.
    • To introduce an improved variant, SphereFace-R, with enhanced stability and performance.

    Main Methods:

    • Developed a unified framework for analyzing large angular margins in hyperspherical face recognition.
    • Introduced SphereFace-R, incorporating two novel multiplicative margin implementations.
    • Investigated SphereFace-R under various feature normalization schemes and employed "characteristic gradient detachment" for training stabilization.

    Main Results:

    • SphereFace-R demonstrates substantially improved training stability compared to SphereFace.
    • The proposed methods and SphereFace-R achieve competitive or superior performance against state-of-the-art face recognition techniques.
    • Experiments validate the effectiveness of SphereFace-R across different feature normalization strategies.

    Conclusions:

    • SphereFace-R offers a more stable and effective approach to hyperspherical face recognition.
    • The unified framework provides valuable insights into optimizing angular margin-based methods.
    • This work advances the practical applicability of deep face recognition in open-set scenarios.