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Detection and Rectification of Distorted Fingerprints.

Xuanbin Si, Jianjiang Feng, Jie Zhou

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    |September 10, 2015
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    This study introduces new algorithms to detect and correct elastic fingerprint distortion using a single image. These methods improve accuracy in fingerprint recognition systems, especially for security applications like watchlists.

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    Area of Science:

    • Biometrics
    • Computer Vision
    • Pattern Recognition

    Background:

    • Elastic distortion in fingerprints is a primary cause of false non-matches in recognition systems.
    • This distortion poses significant risks in negative recognition applications, enabling evasion by malicious users.
    • Accurate fingerprint recognition is critical for security applications like watchlist screening and deduplication.

    Purpose of the Study:

    • To develop novel algorithms for detecting and rectifying elastic skin distortion from single fingerprint images.
    • To enhance the reliability and accuracy of automated fingerprint identification systems (AFIS).
    • To address the challenge of intentional fingerprint distortion aimed at evading identification.

    Main Methods:

    • Distortion detection framed as a two-class classification problem using ridge orientation and period maps with a Support Vector Machine (SVM) classifier.
    • Distortion rectification approached as a regression problem, estimating the distortion field from a distorted fingerprint.
    • Utilizing a reference database of distorted fingerprints and their corresponding distortion fields for rectification.

    Main Results:

    • Algorithms demonstrated promising performance in detecting and correcting fingerprint distortion.
    • Successful validation on diverse datasets including FVC2004 DB1, Tsinghua Distorted Fingerprint database, and NIST SD27.
    • The proposed methods effectively transform distorted fingerprints into a normalized state for improved matching.

    Conclusions:

    • The developed algorithms offer a robust solution for handling elastic distortion in fingerprint recognition.
    • The approach significantly mitigates false non-matches caused by fingerprint distortion.
    • This research contributes to more secure and reliable biometric identification systems, particularly in high-stakes applications.