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This study introduces a novel touchless palm print recognition system using smartphones. The system achieves a 98.64% recognition rate, enhancing mobile biometrics with privacy and speed.

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Area of Science:

  • Biometric systems
  • Mobile security
  • Image processing

Background:

  • Mobile biometrics are trending, necessitating privacy-conscious solutions.
  • Touchless systems address public demand for hygiene and privacy in biometric identification.
  • Existing mobile biometric methods require robust algorithms for accurate, touchless recognition.

Purpose of the Study:

  • To propose a novel touchless palm print recognition system utilizing smartphone imaging.
  • To develop efficient algorithms for hand tracking, image enhancement, and fast computation for mobile biometrics.
  • To enhance the accuracy and speed of mobile-based biometric identification.

Main Methods:

  • Developed a touchless palm print recognition system using smartphone cameras.
  • Implemented a sliding neighborhood operation with local histogram equalization and LHEAT for image enhancement.
  • Introduced an improved fuzzy-based k nearest centroid neighbor (IFkNCN) classifier for accelerated recognition.

Main Results:

  • Achieved a high recognition accuracy of 98.64% for the touchless palm print system.
  • The LHEAT approach effectively enhanced low-quality palm print images.
  • The IFkNCN classifier demonstrated faster computation by reducing training data and removing outliers.

Conclusions:

  • The proposed touchless palm print recognition system is effective for mobile applications.
  • The combination of LHEAT and IFkNCN offers a promising solution for secure and efficient mobile biometrics.
  • This research advances the field of mobile biometrics with a focus on privacy and performance.