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Fast and accurate face recognition system using MORSCMs-LBP on embedded circuits.

Khalid M Hosny1, Aya Y Hamad2, Osama Elkomy1

  • 1Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt.

Peerj. Computer Science
|July 25, 2022
PubMed
Summary

This study introduces MORSCMs-LBP, an efficient facial recognition system using Raspberry Pi. It combines local and global features for accurate, contactless authentication, enhancing security and hygiene.

Keywords:
Face recognitionLocal binary patternMORSCMsRaspberry pi

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Area of Science:

  • Computer Science
  • Biometrics
  • Artificial Intelligence

Background:

  • COVID-19 pandemic necessitates contactless technologies.
  • Traditional biometric systems (fingerprint, PIN) pose infection risks.
  • Advancements in embedded systems like Raspberry Pi enable sophisticated applications.

Purpose of the Study:

  • To develop an efficient, contactless facial recognition system.
  • To integrate local and global feature extraction for improved accuracy.
  • To implement the system on a low-power embedded device.

Main Methods:

  • Developed MORSCMs-LBP approach combining Local Binary Pattern (LBP) and radial substituted Chebyshev moments (MORSCMs).
  • Implemented the system on a Raspberry Pi 4 using C++ and OpenCV.
  • Extracted local and global features to create a unified feature vector.

Main Results:

  • Achieved high accuracy on benchmark datasets: 99.03% (face95), 99.44% (face96), and 100% (grimace).
  • Demonstrated superior performance compared to other recent facial recognition methods.
  • Successfully implemented on resource-constrained Raspberry Pi hardware.

Conclusions:

  • The MORSCMs-LBP approach offers an effective and efficient solution for contactless facial recognition.
  • This method provides a viable alternative to traditional, touch-based biometric systems.
  • The system's performance on embedded hardware highlights its practical applicability.