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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Reconstruction based finger-knuckle-print verification with score level adaptive binary fusion.

Guangwei Gao, Lei Zhang, Jian Yang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |September 18, 2013
    PubMed
    Summary

    Finger knuckle print (FKP) verification is user-friendly but sensitive to pose variations. A new reconstruction method and adaptive fusion rule significantly improve FKP authentication accuracy by reducing false rejections.

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    Area of Science:

    • Biometrics
    • Pattern Recognition
    • Computer Vision

    Background:

    • Finger knuckle print (FKP) is a novel biometrics identifier for personal authentication.
    • FKP offers user-friendly data collection but suffers from pose variations in query images.
    • Existing Gabor filtering methods are sensitive to these variations, leading to high false rejection rates.

    Purpose of the Study:

    • To address the challenge of pose variations in FKP verification.
    • To improve the accuracy of FKP-based authentication systems.
    • To reduce false rejections without significantly increasing false acceptances.

    Main Methods:

    • A dictionary learning-based approach to reconstruct query FKP images.
    • Reconstruction aims to minimize pose variation effects on matching distances.
    • A score-level adaptive binary fusion rule combines pre- and post-reconstruction matching scores.

    Main Results:

    • The proposed reconstruction method effectively reduces matching distance enlargement due to pose variations.
    • The adaptive fusion rule successfully balances false rejection and false acceptance rates.
    • Experimental results on the PolyU FKP database demonstrate significant improvements in FKP verification accuracy.

    Conclusions:

    • The proposed method enhances FKP verification accuracy by mitigating pose variations.
    • The combination of reconstruction and adaptive fusion offers a robust solution for FKP authentication.
    • This approach shows promise for more reliable biometric systems using FKP.