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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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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...
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Reference point detection for camera-based fingerprint image based on wavelet transformation.

Mohammed S Khalil1,2

  • 1Faculty of Commerce and Economics, Sana'a University, Sana'a, Yemen. sayimkhalil@gmail.com.

Biomedical Engineering Online
|May 1, 2015
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Summary
This summary is machine-generated.

This study introduces a new core-point detection method for camera phone fingerprint images, achieving promising accuracy for mobile biometrics. The technique shows effectiveness in both controlled and uncontrolled environments.

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Area of Science:

  • Biometrics
  • Image Processing
  • Pattern Recognition

Background:

  • Core-point detection is crucial for fingerprint recognition systems, serving as a reference for template matching.
  • Existing core-point detection methods are primarily designed for scanner-based images, limiting their application to mobile devices.
  • This research addresses the need for effective core-point detection in fingerprint images acquired via camera phones.

Purpose of the Study:

  • To investigate the feasibility of applying a core-point detection method to fingerprint images captured by camera phones.
  • To develop and evaluate a novel core-point detection technique suitable for mobile biometric applications.

Main Methods:

  • A discrete wavelet transform is employed to extract ridge information from color fingerprint images.
  • The method's performance is assessed using accuracy and consistency metrics, automatically compared against established core points.

Main Results:

  • The proposed method was tested on datasets from 13 subjects in controlled and uncontrolled environments.
  • A detection rate of 82.98% was achieved in a controlled environment.
  • A detection rate of 78.21% was obtained in an uncontrolled environment.

Conclusions:

  • The developed core-point detection method demonstrates promising results for fingerprint images acquired using camera phones.
  • The proposed technique outperforms existing methods in the context of mobile fingerprint recognition.