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    A novel symmetry-driven Siamese network (SDSN) enhances Terahertz (THz) concealed object verification by leveraging unique THz image properties and symmetry. This approach significantly improves detection accuracy while reducing false alarms in security inspections.

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    Area of Science:

    • Computer Vision
    • Imaging Science
    • Security Technology

    Background:

    • Current security inspection methods struggle with Terahertz (THz) image characteristics and are susceptible to noise and pose variations.
    • Existing algorithms, often designed for natural images, fail to capture the unique properties and imaging principles of THz data.
    • Robustness issues in security inspections stem from limitations in handling THz image specifics and environmental factors like noise and subject pose.

    Purpose of the Study:

    • To develop a robust security inspection method specifically for THz concealed object verification.
    • To address the challenges posed by the unique imaging principles of THz data and variations in noise and pose.
    • To introduce a novel deep learning architecture that enhances the accuracy and reliability of THz security screening.

    Main Methods:

    • A symmetry-driven Siamese network (SDSN) was designed, incorporating Contrastive loss for feature extraction and symmetrical prior information.
    • Adaptive Identity Normalization (A-IDN) was proposed to normalize symmetrical metrics, mitigating noise and pose variation impacts.
    • An Adaptive Selective Threshold based on Gaussian Mixture Model (AST-GMM) was developed for classification, enhancing network generalization.

    Main Results:

    • The proposed SDSN significantly improved accuracy in THz concealed object verification tasks.
    • Experiments demonstrated that SDSN outperforms state-of-the-art methods that do not utilize symmetrical prior information.
    • The method showed enhanced robustness against noise interference and pose variations compared to existing approaches.

    Conclusions:

    • The developed SDSN effectively addresses key challenges in THz security inspection, offering a more robust and accurate solution.
    • Leveraging symmetrical prior information and specialized normalization techniques proves crucial for effective THz image analysis.
    • The findings suggest a promising new direction for improving automated security screening systems using THz imaging technology.