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The cross product is a fundamental concept in vector algebra that is a vector operation on two different vectors to obtain a third vector. Unlike the scalar product, the cross product results in a vector quantity perpendicular to both the original vectors.
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Deep Cross-View Reconstruction GAN Based on Correlated Subspace for Multi-View Transformation.

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    This study introduces a novel image transformation framework to improve face recognition across different camera types, like near-infrared (NIR) and thermal (TH), overcoming challenges posed by varying lighting conditions.

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    Area of Science:

    • Computer Vision
    • Biometrics
    • Image Processing

    Background:

    • Visible spectrum (VIS) face identification is limited in poor lighting.
    • Near-infrared (NIR) and thermal (TH) imaging offer alternatives but face domain shift challenges.
    • Existing methods struggle with cross-domain face matching due to unique data distributions.

    Purpose of the Study:

    • To propose a novel image transformation framework for robust cross-domain face recognition.
    • To enhance face matching accuracy between visible, near-infrared, and thermal imaging modalities.
    • To generate high-quality, identity-preserving images across different spectral domains.

    Main Methods:

    • Feature extraction from input images.
    • A transformation network generates target domain images with perceptual fidelity.
    • A reconstruction network preserves original domain information.
    • Framework applied to pix2pix and CycleGAN models (CRC-pix2pix, CRC-CycleGAN).
    • Utilizes paired data considering feature correlation between domains.

    Main Results:

    • Generated high-quality images that preserve original face identity.
    • Demonstrated superior performance in generated image face matching on TFW and BUAA NIR-VIS datasets.
    • Achieved excellent evaluation metrics including SSIM, MSE, PSNR, and LPIPS.
    • Introduced the CQUPT-VIS-TH dataset for thermal-visual face data.

    Conclusions:

    • The proposed framework effectively addresses cross-domain face recognition challenges.
    • CRC-pix2pix and CRC-CycleGAN models show significant improvements in face matching.
    • The approach is versatile and extensible to other image-to-image translation models.
    • The new dataset facilitates further research in multi-modal face recognition.