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Pseudo Label Association and Prototype-Based Invariant Learning for Semi-Supervised NIR-VIS Face Recognition.

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    This study introduces a semi-supervised method for Near-InfraRed and Visible (NIR-VIS) face recognition, reducing the need for extensive labeled data. The novel approach achieves performance comparable to supervised methods, even without identity labels.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Biometrics

    Background:

    • Near-InfraRed and Visible (NIR-VIS) face recognition relies heavily on large labeled datasets.
    • Acquiring and annotating cross-domain data for NIR-VIS face recognition is costly and time-consuming.
    • Existing methods struggle with the domain shift inherent in NIR-VIS face data.

    Purpose of the Study:

    • To develop a semi-supervised approach for NIR-VIS Heterogeneous Face Recognition (NIR-VIS-sHFR).
    • To address the challenge of limited labeled data in cross-domain face recognition tasks.
    • To propose a novel method that learns robust representations without requiring explicit identity labels for all data.

    Main Methods:

    • Proposed a novel pseudo Label association and Prototype-based invariant Learning (LPL) framework.
    • Implemented Cross-domain pseudo Label Association (CLA) for iterative pseudo-label generation and cross-domain model development.
    • Introduced Intra-domain Compact Representation learning (ICR) for feature separation and aggregation within clusters.
    • Utilized Prototype-based Inter-domain Invariant learning (PII) to learn domain-invariant features using cross-domain prototypes.

    Main Results:

    • The semi-supervised LPL method achieved performance comparable to supervised learning approaches.
    • Demonstrated the ability to learn robust cross-domain representations effectively.
    • Showcased successful NIR-VIS face recognition even without complete identity label information.
    • Validated the approach on multiple challenging NIR-VIS datasets.

    Conclusions:

    • The proposed LPL method offers an effective semi-supervised solution for NIR-VIS face recognition.
    • It significantly reduces the dependency on large amounts of labeled training data.
    • The framework successfully learns domain-invariant features, enhancing recognition robustness across different domains.