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Face Biometric Spoof Detection Method Using a Remote Photoplethysmography Signal.

Seung-Hyun Kim1, Su-Min Jeon1, Eui Chul Lee2

  • 1Department of AI & Informatics, Graduate School, Sangmyung University, Hongjimun 2-Gil 20, Jongno-Gu, Seoul 03016, Korea.

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Summary

This study introduces a novel remote photoplethysmography (rPPG) method for face recognition spoofing detection. The technique enhances security against replay attacks using only an RGB camera, achieving 99.74% accuracy.

Keywords:
convolutional neural networkface anti-spoofingface recognitionlong short-term memoryremote photoplethysmography

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Area of Science:

  • Biometrics and Security Engineering
  • Computer Vision and Image Processing
  • Signal Processing for Health

Background:

  • Face recognition systems are vulnerable to spoofing attacks due to the inherent exposure of facial features.
  • Existing remote photoplethysmography (rPPG) methods for spoof detection are susceptible to replay attacks and database dependencies.
  • The need for robust, device-agnostic anti-spoofing solutions is critical for secure facial recognition.

Purpose of the Study:

  • To propose a novel rPPG-based face spoofing detection method resilient to replay attacks and database dependencies.
  • To develop a system usable in mobile environments without requiring additional hardware.
  • To address previously unaddressed spoofing attack scenarios.

Main Methods:

  • Utilized an RGB camera to capture remote photoplethysmography signals from the face.
  • Analyzed both time-series and frequency features of the rPPG signals for spoof detection.
  • Incorporated specific countermeasures against known and novel attack vectors.

Main Results:

  • Achieved a high spoof detection accuracy of 99.7424%.
  • Demonstrated robustness against high-quality replay attacks.
  • Verified the method's effectiveness in mobile environments using only an RGB camera.
  • Identified and analyzed potential counter-attack scenarios in specific facial regions.

Conclusions:

  • The proposed rPPG-based method offers a significant advancement in face recognition anti-spoofing.
  • The system provides a practical, high-accuracy solution deployable on standard mobile devices.
  • Further research can explore mitigating the identified cut-off attack vulnerabilities.