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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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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...
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The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
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Efficient Hardware Implementation For Fingerprint Image Enhancement Using Anisotropic Gaussian Filter.

Tariq Mahmood Khan, Donald G Bailey, Mohammad A U Khan

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |February 27, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel, efficient hardware implementation for real-time fingerprint image enhancement using a modified anisotropic Gaussian filter. The proposed method significantly speeds up processing time and improves FPGA resource utilization for faster, more accessible biometric identification.

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    Area of Science:

    • Computer Science
    • Electrical Engineering
    • Biometrics

    Background:

    • Hardware implementation of fingerprint filtering is often expensive and complex.
    • Real-time processing demands are critical for efficient biometric systems.

    Purpose of the Study:

    • To propose a cost-effective, real-time hardware technique for fingerprint image enhancement.
    • To overcome the limitations of existing expensive hardware solutions.

    Main Methods:

    • A modified anisotropic Gaussian filter was decomposed into orthogonal Gaussians and an oriented line Gaussian for hardware efficiency.
    • A dynamic orientation control architecture was developed for the line Gaussian filter.
    • Local image normalization was employed for input image homogenization.

    Main Results:

    • The proposed structure achieved parallel, compute-intensive, and real-time processing on a mid-range FPGA.
    • Significant improvements in image-processing speed and FPGA resource utilization were observed.
    • The hardware architecture demonstrated enhanced speed while maintaining effective image enhancement benchmarks.

    Conclusions:

    • The developed technique offers a faster and more resource-efficient hardware solution for fingerprint image enhancement.
    • This approach makes advanced fingerprint filtering more accessible for real-time biometric applications.
    • The study successfully addresses the challenge of expensive hardware implementations in fingerprint processing.