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Robust Cross-Domain Pseudo-Labeling and Contrastive Learning for Unsupervised Domain Adaptation NIR-VIS Face

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    This study introduces a novel Robust cross-domain Pseudo-labeling and Contrastive learning (RPC) network for unsupervised Near-infrared and Visible (NIR-VIS) face recognition. The RPC network achieves over 99% accuracy in pseudo-label assignment, enabling efficient large-scale face recognition systems.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Biometrics

    Background:

    • Near-infrared and visible (NIR-VIS) face recognition is crucial for 24-hour security, especially in low-light conditions.
    • Manual annotation of large, heterogeneous face datasets for NIR-VIS recognition is costly and time-consuming, hindering real-world applications.
    • Unsupervised domain adaptation offers a promising approach to overcome annotation limitations in NIR-VIS face recognition.

    Purpose of the Study:

    • To develop an unsupervised domain adaptation method for NIR-VIS face recognition.
    • To eliminate the need for manual identity labels in large-scale NIR-VIS face recognition systems.
    • To propose a novel network architecture that effectively handles domain discrepancies between NIR and VIS face images.

    Main Methods:

    • A novel Robust cross-domain Pseudo-labeling and Contrastive learning (RPC) network is proposed.
    • Key components include NIR cluster-based Pseudo labels Sharing (NPS) for generating reliable pseudo-labels, Domain-specific cluster Contrastive Learning (DCL) for learning discriminative intra-domain representations, and Inter-domain cluster Contrastive Learning (ICL) for robust, domain-independent feature learning.
    • The NPS component leverages NIR clusters to share label knowledge with the VIS domain, while DCL and ICL refine representations through contrastive learning strategies.

    Main Results:

    • The proposed RPC network achieved over 99% accuracy in pseudo-label assignment.
    • Experimental results on four mainstream NIR-VIS datasets demonstrate the advanced performance of the RPC network.
    • The method successfully learns robust and domain-independent representations without manual annotations.

    Conclusions:

    • The RPC network effectively addresses the challenges of unsupervised domain adaptation in NIR-VIS face recognition.
    • The proposed approach significantly reduces the reliance on manual annotations, paving the way for scalable real-world applications.
    • This work advances the state-of-the-art in NIR-VIS face recognition by enabling accurate and efficient recognition under varying lighting conditions.