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Automated spoof-detection for fingerprints using optical coherence tomography.

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    This study introduces a new method for detecting and extracting fingerprint data from both surface and internal skin layers using optical coherence tomography. Blending these layers enhances fingerprint quality and improves spoof detection capabilities.

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    Area of Science:

    • Biometrics
    • Medical Imaging
    • Forensic Science

    Background:

    • Optical coherence tomography (OCT) offers 3D skin imaging, revealing identical surface and internal fingerprints.
    • Surface fingerprints are vulnerable to damage, distortion, and spoofing.
    • Internal fingerprints are challenging to access and extract effectively.

    Purpose of the Study:

    • To develop a novel scaling-resolution approach for fingerprint zone detection and extraction.
    • To introduce a local-quality-based blending procedure for combining surface and internal fingerprint data.
    • To enhance fingerprint image quality and improve spoof detection.

    Main Methods:

    • A novel scaling-resolution algorithm for fingerprint zone detection and extraction.
    • A local-quality-based blending procedure to merge surface and internal fingerprint data.
    • Evaluation of zone detection accuracy using mean-squared error and structural similarity.
    • Assessment of blended fingerprint quality using NIST Fingerprint Image Quality scores and match scores.

    Main Results:

    • Zone detection accuracy comparable to prior work (MSE 25.9, SSIM 95.8%).
    • Blending surface and internal fingerprints improved NIST scores and average maximum match scores.
    • The blending procedure effectively combined high-quality regions, mitigating surface artifacts.
    • Proposed spoof detection via surface-to-internal fingerprint comparison demonstrated effectiveness.

    Conclusions:

    • The novel approach successfully extracts and blends surface and internal fingerprint data, enhancing overall quality.
    • The blending technique overcomes limitations of surface-only fingerprint analysis.
    • The method offers improved robustness against spoofing attempts through comparative analysis.