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Fuzzy Sparse Autoencoder Framework for Single Image Per Person Face Recognition.

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    This study introduces a new two-layer framework for single sample per person (SSPP) face recognition. It effectively combines local and global features, outperforming existing methods for robust face identification.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Biometrics

    Background:

    • Single Sample Per Person (SSPP) face recognition is a challenging problem due to limited data per individual.
    • Patch/local-based algorithms are common but often overlook crucial global discriminative information.
    • Nondiscriminative facial regions can hinder recognition accuracy in existing methods.

    Purpose of the Study:

    • To propose a novel two-layer local-to-global feature learning framework for SSPP face recognition.
    • To integrate both local robustness and global discriminative power for improved face identification.
    • To develop a shallow network architecture to mitigate overfitting in SSPP tasks.

    Main Methods:

    • A patch-based fuzzy rough set feature selection strategy is employed in the first layer to learn objective-oriented local features.
    • These local features are robust to variations and preserve patch discrimination.
    • A sparse autoencoder is utilized in the second layer to extract global structural information from local features, reducing the impact of nondiscriminative regions.

    Main Results:

    • The proposed framework successfully combines local and global feature learning.
    • It effectively reduces the negative impact of nondiscriminative facial regions.
    • Experimental results demonstrate superior performance compared to state-of-the-art feature learning algorithms for SSPP face recognition.

    Conclusions:

    • The novel two-layer local-to-global feature learning framework offers a significant advancement in SSPP face recognition.
    • The integration of local and global information enhances recognition accuracy and robustness.
    • The shallow network design effectively addresses overfitting issues common in SSPP problems.