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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.
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Related Experiment Video

Updated: Jun 5, 2026

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

Published on: September 2, 2020

Fingerprint indexing based on Minutia Cylinder-Code.

Raffaele Cappelli¹, Matteo Ferrara, Davide Maltoni

¹DEIS-Università di Bologna, via Sacchi 3, Cesena (FC) 47521, Italy. raffaele.cappelli@unibo.it

IEEE Transactions on Pattern Analysis and Machine Intelligence

|December 22, 2010

Summary

This summary is machine-generated.

This study introduces a novel hash-based indexing method for faster fingerprint identification. The new approach, using Locality-Sensitive Hashing (LSH) with Minutiae Cylinder-Code (MCC), outperforms existing methods in large databases.

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Area of Science:

Biometrics
Computer Science
Pattern Recognition

Background:

Fingerprint identification systems require efficient indexing for large databases.
Existing methods often rely on complex feature sets, impacting performance.

Purpose of the Study:

To develop a novel hash-based indexing method for accelerated fingerprint identification.
To improve the efficiency and accuracy of large-scale fingerprint matching.

Main Methods:

A Locality-Sensitive Hashing (LSH) scheme was designed using Minutiae Cylinder-Code (MCC).
MCC maps minutiae-based representations to fixed-length, transformation-invariant binary vectors.
A new search algorithm was developed based on a numerical approximation of MCC vector similarity.

Main Results:

The proposed method was compared against 15 existing fingerprint indexing techniques.
The new approach demonstrated superior performance across typical fingerprint indexing benchmarks.
Outperformance was achieved despite utilizing a smaller feature set compared to other leading methods.

Conclusions:

The novel hash-based indexing method significantly enhances fingerprint identification speed.
The LSH scheme with MCC offers an effective and efficient solution for large-scale biometric databases.
This approach represents a promising advancement in biometric security and identification.