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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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A device engineer plays a crucial role in designing user interfaces for mobile devices. One such interface is the resistive touchscreen, which fundamentally consists of two metallic layers: a flexible upper layer and a rigid lower layer, separated by a narrow gap. The high resistance between these two layers is a key characteristic of this design.
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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Palmprint Recognition Across Different Devices.

Wei Jia1, Rong-Xiang Hu, Jie Gui

  • 1Institute of Nuclear Energy Safety Technology, Chinese Academy of Science, Hefei 230031, China. icg.jiawei@gmail.com

Sensors (Basel, Switzerland)
|September 13, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new approach for Palmprint Recognition Across Different Devices (PRADD), creating a unique database and proposing a scale normalization method. Orientation coding methods show promising results for cross-device palmprint recognition.

Keywords:
biometricsdifferent devicespalmprint recognitionsensors

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Area of Science:

  • Biometrics
  • Computer Vision
  • Pattern Recognition

Background:

  • Palmprint Recognition Across Different Devices (PRADD) is an under-researched area.
  • Existing public databases for PRADD are unavailable.
  • Non-contact palmprint acquisition introduces challenges like rotation and scale variations.

Purpose of the Study:

  • To investigate the challenges of Palmprint Recognition Across Different Devices (PRADD).
  • To create a novel non-contact PRADD image database.
  • To propose and evaluate methods for robust cross-device palmprint recognition.

Main Methods:

  • Development of a non-contact PRADD image database with 12,000 grayscale images from 100 subjects using three devices.
  • Proposal of a robust palm width calculation method for scale normalization.
  • Evaluation of subspace learning, correlation, and orientation coding methods on the created database.

Main Results:

  • Orientation coding based methods demonstrated superior recognition performance for PRADD.
  • The proposed palm width normalization method effectively addresses scale changes.
  • The created PRADD database serves as a valuable resource for future research.

Conclusions:

  • Orientation coding methods are effective for cross-device palmprint recognition.
  • Scale normalization is crucial for improving PRADD accuracy.
  • The developed database and methods advance the field of biometrics.