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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: Jun 9, 2026

Super-resolution Imaging of Neuronal Dense-core Vesicles
09:30

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Published on: July 2, 2014

Multifeature-based high-resolution palmprint recognition.

Jifeng Dai1, Jie Zhou

  • 1Department of Automation, Tsinghua University, Beijing 100084, China. djf05@mails.tsinghua.edu.cn

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 25, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new high-resolution palmprint recognition algorithm using multiple features for improved accuracy in access control and forensic applications. The novel approach enhances matching performance and identification rates for secure biometric systems.

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Last Updated: Jun 9, 2026

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Photoactivated Localization Microscopy with Bimolecular Fluorescence Complementation (BiFC-PALM)
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Photoactivated Localization Microscopy with Bimolecular Fluorescence Complementation (BiFC-PALM)

Published on: December 22, 2015

Area of Science:

  • Biometrics
  • Computer Vision
  • Pattern Recognition

Background:

  • Palmprint recognition is crucial for access control and forensics.
  • Existing methods focus on low-resolution palmprints, limiting forensic applications.
  • High-resolution palmprints (500+ ppi) offer richer information for enhanced security.

Purpose of the Study:

  • Propose a novel algorithm for high-resolution palmprint recognition.
  • Improve matching performance and identification accuracy in biometric systems.
  • Address limitations of conventional methods in forensic and high-security scenarios.

Main Methods:

  • Utilize multiple features: minutiae, density, orientation, and principal lines.
  • Develop a quality-based, adaptive orientation field estimation algorithm for crease-rich regions.
  • Implement a novel fusion scheme for identification, outperforming traditional methods.

Main Results:

  • The proposed algorithm significantly improves palmprint matching performance.
  • Achieved a 17% lower False Rejection Rate (FRR) than existing algorithms at a False Acceptance Rate (FAR) of 10^-5.
  • Increased rank-1 live-scan partial palmprint recognition rate from 82.0% to 91.7%.

Conclusions:

  • The novel algorithm demonstrates superior performance for high-resolution palmprint recognition.
  • Multi-feature integration and advanced fusion schemes enhance biometric security.
  • Density feature proves highly discriminative for palmprint identification.