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IR Frequency Region: Fingerprint Region01:03

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 23, 2026

Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation
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U-Net-Based Fingerprint Enhancement for 3D Fingerprint Recognition.

Mohammad Mogharen Askarin1, Min Wang1,2, Xuefei Yin3

  • 1School of Systems and Computing, University of New South Wales, Canberra, ACT 2612, Australia.

Sensors (Basel, Switzerland)
|March 17, 2025
PubMed
Summary
This summary is machine-generated.

This study enhances 3D fingerprint recognition using deep learning U-Net for improved contrast. This advanced biometric authentication method significantly reduces the Equal Error Rate (EER), making it more secure and reliable.

Keywords:
3Dbiometricsdeep learningfingerprint

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Area of Science:

  • Biometrics and Security Engineering
  • Computer Vision
  • Machine Learning

Background:

  • Conventional 2D fingerprint biometrics face sensor spoofing and disease transmission risks due to contact-based sensors.
  • Three-dimensional (3D) fingerprint recognition offers contactless capture and enhanced security against spoofing.
  • Converting 3D fingerprint data (point clouds) to 2D images often results in poor contrast, hindering recognition accuracy.

Purpose of the Study:

  • To develop an effective image segmentation approach for enhancing the contrast of 3D fingerprint images.
  • To improve the performance of conventional 2D fingerprint recognition methods when applied to 3D fingerprint data.
  • To address the limitations of existing 3D to 2D fingerprint conversion processes.

Main Methods:

  • Proposed an image segmentation method utilizing the deep learning U-Net architecture.
  • Applied the U-Net model to enhance the contrast of 2D gray-scale images generated from 3D fingerprint point clouds.
  • Evaluated the enhanced fingerprint images using conventional fingerprint recognition techniques.

Main Results:

  • The proposed U-Net based image segmentation significantly improved fingerprint contrast.
  • Fingerprint recognition Equal Error Rate (EER) decreased from 41.32% to 13.96% in experiment A.
  • EER improved from 41.97% to 12.49% in experiment B, demonstrating substantial performance gains.

Conclusions:

  • Deep learning-based image segmentation effectively enhances 3D fingerprint images for recognition.
  • The proposed method offers a viable solution to improve the accuracy and reliability of 3D fingerprint authentication systems.
  • This approach addresses key challenges in 3D fingerprint recognition, paving the way for more secure and hygienic biometric solutions.