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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

906
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...
906

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Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment
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Handwriting identification and verification using artificial intelligence-assisted textural features.

Heng Zhao1, Huihui Li2

  • 1College of Infommation Engineering and Artificial Inteligence, Zhengzhou Vocational University of Information and Technology, Zhengzhou, 450046, China. zhaoheng188@outlook.com.

Scientific Reports
|December 8, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a Spatial Variation-dependent Verification (SVV) scheme for authenticating digital and handwritten signatures. The method uses textural features and a convolution neural network to improve accuracy and reduce false positives in intelligent control systems.

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Area of Science:

  • Computer Science
  • Automation Engineering
  • Biometrics

Background:

  • Intelligent process control and automation systems require robust signature verification.
  • Digital handwritten signatures exhibit variations in pixel intensity and spatial arrangement due to external factors.
  • Existing verification methods struggle with these inherent fluctuations.

Purpose of the Study:

  • To introduce a novel Spatial Variation-dependent Verification (SVV) scheme.
  • To enhance the accuracy and reliability of signature authentication in automated systems.
  • To address the challenge of pixel intensity and spatial variations in digital signatures.

Main Methods:

  • Utilizing textural features (TF) for signature verification.
  • Employing a convolution neural network (CNN) for layered analysis.
  • Identifying and spatially mapping signature points to detect textural feature matching.
  • Extracting textural features between successive identification points to minimize false positives.

Main Results:

  • The SVV scheme demonstrated effective verification by analyzing pixel intensities and spatial variations.
  • The convolution neural network successfully generated new identification points and selected maximum matching features.
  • The method significantly reduced false positives by avoiding iterated verification for varying textures.
  • Performance was validated using metrics including accuracy, precision, texture detection, false positives, and verification time.

Conclusions:

  • The proposed Spatial Variation-dependent Verification (SVV) scheme offers a reliable method for authenticating digital and handwritten signatures.
  • The integration of textural features and a CNN effectively handles variations in signatures.
  • This approach enhances the security and dependability of intelligent process control and automation systems.