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Efficient internal and surface fingerprint extraction and blending using optical coherence tomography.

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Automated spoof-detection for fingerprints using optical coherence tomography.

Luke Nicholas Darlow, Leandra Webb, Natasha Botha

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    This study introduces an automated method for detecting fake fingerprints using optical coherence tomography (OCT). This advanced technique achieves 100% accuracy in identifying artificial fingerprints without misidentifying real ones.

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    Area of Science:

    • Biometrics
    • Optical Engineering
    • Forensic Science

    Background:

    • Fingerprint recognition systems are widely used in high-security areas.
    • Spoofing these systems with artificial fingerprints presents a growing security challenge.

    Purpose of the Study:

    • To develop an automated method for detecting artificial fingerprints using optical coherence tomography (OCT).
    • To leverage OCT's 3D skin structure imaging for enhanced spoof detection.

    Main Methods:

    • Utilized optical coherence tomography (OCT) to capture 3D representations of skin.
    • Extracted two key features to identify surface layers indicative of artificial fingerprints.

    Main Results:

    • The extracted features demonstrated high separability between real and artificial fingerprints.
    • Achieved 100% accuracy in spoof detection.
    • No false rejections of genuine fingerprints were recorded.

    Conclusions:

    • Automated spoof detection using OCT is highly effective.
    • OCT provides crucial subsurface information for robust biometric security.
    • This research represents the first fully automated OCT-based approach for fingerprint spoof detection.