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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: Jul 13, 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 image reconstruction from standard templates.

R Cappelli¹, A Lumini, D Maltoni

¹Scienze dell'Informazione, Università di Bologna, Cesena, Italy. cappelli@csr.unibo.it

IEEE Transactions on Pattern Analysis and Machine Intelligence

|July 14, 2007

Summary

Reconstructing fingerprint images from compact templates is now possible. These realistic reconstructed fingerprints can potentially deceive automated fingerprint recognition systems, posing a security risk.

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Area of Science:

Biometrics
Computer Vision
Cybersecurity

Background:

Minutiae-based templates are compact fingerprint representations.
Historically, they were considered insufficient for reconstructing original fingerprint images.

Purpose of the Study:

To propose a novel method for reconstructing fingerprint images from standard minutiae-based templates.
To evaluate the similarity between reconstructed and original fingerprint images.
To assess the security implications of this reconstruction technique against fingerprint recognition systems.

Main Methods:

Developed a novel approach to reconstruct fingerprint images from minutiae-based templates.
Quantitatively assessed the similarity between reconstructed and original fingerprint images.
Evaluated the efficacy of the reconstruction technique through simulated masquerade attacks on nine fingerprint recognition algorithms.

Main Results:

Demonstrated the successful reconstruction of realistic fingerprint images from compact templates.
Found that reconstructed images have a high probability of deceiving state-of-the-art commercial fingerprint recognition systems.
Determined that while unlikely to fool human experts, the reconstructed images pose a significant threat to automated systems.

Conclusions:

Minutiae-based templates contain sufficient information for realistic fingerprint image reconstruction.
The proposed reconstruction method presents a viable security concern for automated biometric systems.
Further research is needed to develop countermeasures against such reconstruction-based attacks.