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Related Concept Videos

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.
The...

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Related Experiment Video

Updated: May 8, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Video-based fingerprint verification.

Wei Qin1, Yilong Yin, Lili Liu

  • 1School of Computer Science and Technology, Shandong University, Jinan 250101, Shandong, China. weiqin_wq@163.com

Sensors (Basel, Switzerland)
|September 7, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel video-based fingerprint verification system. Utilizing dynamic and static information from fingerprint videos significantly reduces the equal error rate (EER) compared to traditional methods.

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Area of Science:

  • Biometrics
  • Computer Vision
  • Pattern Recognition

Background:

  • Conventional fingerprint verification relies solely on static image data.
  • Existing systems may have limitations in accuracy and robustness.
  • Dynamic information within fingerprint videos remains largely untapped for verification.

Purpose of the Study:

  • To propose and evaluate a novel fingerprint verification system using dynamic information from fingerprint videos.
  • To enhance the accuracy and reduce error rates in fingerprint identification.
  • To leverage both dynamic and static features for improved biometric security.

Main Methods:

  • Fingerprint videos are captured using standard devices, ensuring a similar user experience.
  • Preprocessing and alignment techniques are applied to the video data.
  • Novel metrics, 'inside similarity' and 'outside similarity', are defined and computed.
  • Match scores are generated by combining dynamic and static similarity measures.

Main Results:

  • The video-based method achieved a 60% relative reduction in the equal error rate (EER) compared to conventional methods.
  • The proposed approach demonstrated superior performance even when time complexity was matched with traditional systems.
  • The method showed improved accuracy over multiple impression fusion techniques.
  • Significantly lower false acceptance rates (FAR) were observed at low false rejection rates (FRR).

Conclusions:

  • Fingerprint videos offer a rich source of dynamic information for enhanced verification.
  • The proposed video-based system provides superior accuracy and reduced error rates.
  • This method represents a significant advancement in biometric security technology.