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    This study introduces a novel framework for face recognition, treating it as a deformable image registration and feature matching problem. The method achieves superior recognition rates without the generalizability issues common in learning-based algorithms.

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    Area of Science:

    • Computer Science
    • Biometrics
    • Image Processing

    Background:

    • Face recognition is a critical biometric task with ongoing challenges in accuracy and generalizability.
    • Existing learning-based algorithms often struggle with variations in facial images, leading to poor performance on unseen data.

    Purpose of the Study:

    • To propose a new framework for face recognition that overcomes the generalizability problem.
    • To formulate face recognition as a groupwise deformable image registration and feature matching problem.

    Main Methods:

    • Representing each pixel with an anatomical signature derived from survival exponential entropy (SEE).
    • Utilizing a Markov random field-based groupwise registration framework for feature-guided deformable image registration.
    • Measuring facial image similarity on a nonlinear Riemannian manifold using deformable transformations.

    Main Results:

    • The proposed method consistently achieved the highest recognition rates across four public databases (FERET, CAS-PEAL-R1, FRGC ver 2.0, LFW).
    • Experimental results demonstrate superior performance compared to several state-of-the-art face recognition approaches.
    • The method showed no susceptibility to the generalizability problem prevalent in learning-based algorithms.

    Conclusions:

    • The proposed framework offers a robust and generalizable solution for face recognition.
    • Formulating face recognition as deformable image registration provides a powerful alternative to traditional learning-based methods.
    • The anatomical signature and registration framework are key to the method's high accuracy and robustness.