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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.
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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
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Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER): A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits
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Latent palmprint matching.

Anil K Jain1, Jianjiang Feng

  • 1Department of Computer Science and Engineering, Michigan State University, 3115 Engineering Building, East Lansing, MI 48824-1226, USA. jain@cse.msu.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|April 18, 2009
PubMed
Summary
This summary is machine-generated.

Forensic palmprint matching is improved with a new system for latent-to-full print analysis. This system achieves high recognition rates, aiding criminal investigations with detailed palmprint identification.

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Area of Science:

  • Forensic Science
  • Biometrics
  • Computer Vision

Background:

  • Palmprints are crucial in forensic investigations, with 30% of crime scene latent prints originating from palms.
  • Existing biometric systems primarily use low-resolution palmprints and full-to-full matching, unsuitable for forensic applications.
  • Latent palmprint matching presents significant challenges due to poor image quality, partial areas, complex backgrounds, numerous minutiae, and creases.

Purpose of the Study:

  • To develop a latent-to-full palmprint matching system for forensic applications.
  • To handle high-resolution palmprints (500 ppi or higher) and utilize minutiae features compatible with forensic expert methodologies.
  • To address the challenges of matching partial and poor-quality latent palmprints against extensive databases.

Main Methods:

  • Developed a robust algorithm for estimating local ridge direction and frequency in palmprints.
  • Implemented minutiae extraction, even from low-quality prints, using a fixed-length descriptor (MinutiaCode).
  • Employed an alignment-based minutiae matching algorithm for comparing palmprints.

Main Results:

  • Tested the system on 150 live-scan partial palmprints and 100 latent palmprints against a database of 10,200 full palmprints.
  • Achieved a rank-1 recognition rate of 78.7% for live-scan partial palmprints.
  • Achieved a rank-1 recognition rate of 69% for latent palmprints, demonstrating effectiveness in challenging forensic scenarios.

Conclusions:

  • The proposed latent-to-full palmprint matching system shows significant promise for forensic applications.
  • The system effectively handles high-resolution palmprints and extracts reliable minutiae features from challenging latent prints.
  • The achieved recognition rates indicate a valuable advancement in forensic palmprint identification technology.