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Robust Face Recognition via Minimum Error Entropy-Based Atomic Representation.

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    This study introduces a novel Minimum Error Entropy-based Atomic Representation (MEEAR) framework for face recognition. MEEAR offers improved robustness to noise and enhances recognition accuracy compared to traditional Mean Square Error methods.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Pattern Recognition

    Background:

    • Representation-based classifiers (RCs) are widely used in face recognition.
    • Existing RCs often rely on Mean Square Error (MSE), which is sensitive to non-Gaussian noise and occlusion.
    • This sensitivity degrades performance in real-world scenarios with corrupted facial images.

    Purpose of the Study:

    • To develop a more robust face recognition framework less susceptible to noise and corruption.
    • To introduce a novel Minimum Error Entropy-based Atomic Representation (MEEAR) framework.
    • To enhance the discriminative power of representation vectors in face recognition.

    Main Methods:

    • Proposed a Minimum Error Entropy-based Atomic Representation (MEEAR) framework.
    • Utilized the minimum error entropy criterion, independent of error distribution assumptions.
    • Minimized atomic norm regularized Renyi's entropy of the reconstruction error.
    • Developed two Minimum Error Entropy (MEE)-based RCs by defining specific atomic sets.

    Main Results:

    • The MEEAR framework demonstrated superior robustness to noise compared to MSE-based RCs.
    • MEEAR achieved higher recognition accuracy on popular face databases.
    • Experimental results showed improved reconstructed image quality alongside recognition accuracy.

    Conclusions:

    • The MEEAR framework offers a robust and effective alternative to traditional MSE-based RCs for face recognition.
    • The minimum error entropy criterion provides better noise resilience.
    • MEEAR serves as a versatile platform for developing advanced face recognition classifiers.