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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

910
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...
910

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Multi-region minutiae depth value-based efficient forged finger print analysis.

M Baskar1, Renuka Devi Rajagopal2, Prasad B V V S3

  • 1Department of Computing Technologies, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamilnadu, India.

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|November 16, 2023
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Summary
This summary is machine-generated.

A new multi-region minutiae depth value (MRMDV) algorithm enhances fingerprint security by detecting forgeries with 98% accuracy. This method preprocesses images and analyzes regional features for reliable biometric authentication.

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Area of Science:

  • Biometrics and Security
  • Image Processing
  • Forensic Science

Background:

  • Biometrics are crucial for security systems, with fingerprints being highly distinctive.
  • Existing fingerprint forgery detection algorithms often lack optimal performance.
  • There is a need for improved methods to ensure the authenticity of fingerprint data.

Purpose of the Study:

  • To introduce a novel algorithm for enhanced fingerprint forgery detection.
  • To address the performance deficiencies of previous fingerprint analysis techniques.
  • To improve the accuracy and efficiency of identifying forged fingerprints.

Main Methods:

  • Applied median and Gabor filters for noise reduction and image sharpening.
  • Segmented preprocessed fingerprint images into multiple regions for detailed analysis.
  • Extracted regional features including ridge ends, bifurcations, enclosures, dots, and islands.
  • Computed Multi-Region Minutiae Depth Value (MRMDV) based on extracted features for forgery assessment.

Main Results:

  • The MRMDV algorithm achieved a high forged fingerprint detection accuracy of up to 98%.
  • The method demonstrated a low time complexity, completing analysis in just 12 seconds.
  • The approach effectively utilizes regional minutiae depth values for robust forgery detection.

Conclusions:

  • The MRMDV-based algorithm offers a significant advancement in forged fingerprint detection.
  • This method provides a reliable and efficient solution for enhancing biometric security.
  • The study highlights the potential of regional feature analysis in advanced biometric systems.