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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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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Published on: August 30, 2013

Core point pixel-level localization by fingerprint features in spatial domain.

Xueyi Ye¹, Yuzhong Shen², Maosheng Zeng¹

¹Lab of Pattern Recognition and Information Security, Hangzhou Dianzi University, Hangzhou 310018, China.

Mathematical Biosciences and Engineering : MBE

|December 14, 2021

Summary

This summary is machine-generated.

This study introduces a novel method for accurate fingerprint core point localization using unique spatial features. The approach enhances fingerprint recognition by reducing errors in singular point detection.

Keywords:

accurate core point location confluence furcation image spatial domain

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Area of Science:

Biometrics
Image Processing
Pattern Recognition

Background:

Singular point detection is crucial for fingerprint recognition, but current methods face challenges with false positives and inaccurate localization.
Existing techniques for core point localization in fingerprints often suffer from displacement issues.

Purpose of the Study:

To propose an accurate core point localization method for fingerprint images.
To address the core point displacement problem in singular point detection.
To improve the robustness and accuracy of fingerprint recognition systems.

Main Methods:

A novel method defining 'furcation' and 'confluence' features to represent ridge/valley distribution.
Extraction of the innermost ridge curve, with its summit identified as the core point.
Development of an approach to remove false furcation and confluence features based on correlations.

Main Results:

The proposed method achieves satisfactory core localization accuracy across a large number of fingerprint samples.
Experimental results demonstrate improved accuracy compared to existing methods.
Enhanced robustness against false positive singular points.

Conclusions:

The novel spatial domain feature-based method offers an accurate and robust solution for fingerprint core point localization.
This approach effectively mitigates core point displacement issues in singular point detection.
The findings contribute to advancements in reliable fingerprint recognition systems.