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IR Frequency Region: Fingerprint Region

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.
The...

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Updated: Jun 12, 2026

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

Rolled fingerprint construction using MRF-based nonrigid image registration.

Dongjin Kwon¹, Il Dong Yun, Sang Uk Lee

¹Department of Electrical Engineering and Computer Science, ASRI, Seoul National University, Korea. djk@cvl.snu.ac.kr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|June 10, 2010

Summary

This summary is machine-generated.

This study introduces a novel rolled fingerprint construction method using advanced nonrigid image registration. The technique accurately preserves fingerprint geometry, outperforming traditional ink-based methods.

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Last Updated: Jun 12, 2026

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

Area of Science:

Biometrics
Computer Vision
Image Processing

Background:

Traditional rolled fingerprint acquisition can distort geometric properties.
Existing digital rolled fingerprint construction methods have limitations in accuracy.

Purpose of the Study:

To propose a novel, accurate rolled fingerprint construction method.
To improve the geometric fidelity of synthesized rolled fingerprints.

Main Methods:

Utilized a state-of-the-art nonrigid image registration algorithm.
Employed a Markov random field (MRF) energy model for dense correspondence.
Warped fingerprint image sequences to synthesize a rolled print.

Main Results:

The proposed method generates rolled fingerprints with superior geometric accuracy.
Comparative experiments demonstrated significant advantages over existing approaches.
Preserves the natural geometric properties of the finger surface.

Conclusions:

The novel MRF-based registration method offers a more accurate approach to rolled fingerprint construction.
This technique advances digital fingerprint acquisition by preserving surface geometry.
The method shows significant potential for improving biometric identification systems.