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Improving Shadow Suppression for Illumination Robust Face Recognition.

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    This study introduces a new model-driven method for face image lighting normalization. The approach generates a Chromaticity Intrinsic Image (CII) robust to illumination changes, improving face recognition accuracy.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Biometrics

    Background:

    • 2D face analysis is sensitive to uncontrolled real-world illumination.
    • Data-driven methods struggle with infinite lighting variations.
    • Illumination-robust preprocessing is a significant challenge for reliable face analysis.

    Purpose of the Study:

    • To propose a novel model-driven approach for face image lighting normalization.
    • To develop a method that improves robustness to illumination variations in face analysis.
    • To enhance the performance of face recognition under diverse lighting conditions.

    Main Methods:

    • Building an underlying reflectance model of skin-camera-light interactions.
    • Elaborating the formation of face color appearance.
    • Generating a Chromaticity Intrinsic Image (CII) in a log chromaticity space.
    • Reconstructing photo-realistic color face images, removing shadows while preserving details.

    Main Results:

    • The proposed pipeline generates a Chromaticity Intrinsic Image (CII) robust to illumination variations.
    • Reconstructed images eliminate shadows and retain color and identity details.
    • Experimental results demonstrate effectiveness in face recognition under various lighting conditions, including soft and hard shadows.

    Conclusions:

    • The novel model-driven approach effectively addresses illumination challenges in face analysis.
    • The method offers an advantage over prevailing techniques by reconstructing realistic images.
    • The technique shows significant promise for reliable face recognition in real-world scenarios.