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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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Two-level evaluation on sensor interoperability of features in fingerprint image segmentation.

Gongping Yang¹, Ying Li, Yilong Yin

¹School of Computer Science and Technology, Shandong University, Jinan 250101, Shandong, China. gpyang@sdu.edu.cn

Sensors (Basel, Switzerland)

|June 28, 2012

Summary

This study addresses fingerprint segmentation challenges across different sensors. A novel two-level feature evaluation method improves accuracy by assessing feature adaptability, enhancing overall fingerprint recognition performance.

Keywords:

decision tree feature evaluation fingerprint segmentation segmentation error rate sensor interoperability

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Area of Science:

Biometrics
Image Processing
Pattern Recognition

Background:

Fingerprint segmentation performance is highly dependent on the features used.
Features effective for one sensor may not generalize to others, leading to performance degradation.
The sensor interoperability of segmentation features is a critical issue in biometrics.

Purpose of the Study:

To empirically analyze the sensor interoperability problem of fingerprint segmentation features.
To propose and validate a two-level feature evaluation method for assessing feature adaptability across different sensors.
To enhance the robustness and accuracy of fingerprint segmentation systems.

Main Methods:

A two-level feature evaluation approach was developed.
The first level involved evaluating features based on segmentation error rate.
The second level utilized a decision tree for feature evaluation.

Main Results:

The proposed method was tested on multiple fingerprint databases from various sensors.
Experimental results demonstrated the method's effectiveness in evaluating feature sensor interoperability.
Features selected using the proposed method achieved superior segmentation accuracy across different sensors.

Conclusions:

The developed two-level feature evaluation method effectively assesses the sensor interoperability of fingerprint segmentation features.
The method enables the selection of features that generalize well across different sensors.
This leads to improved segmentation accuracy and overall performance for cross-sensor fingerprint recognition systems.