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Face liveness detection using defocus.

Sooyeon Kim1, Yuseok Ban2, Sangyoun Lee3

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This study introduces a novel face liveness detection method using defocus effects to combat spoofing attacks in face recognition (FR) systems. The technique achieves a 3.29% half total error rate (HTER), enhancing security for identity authentication.

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Area of Science:

  • Computer Science
  • Biometrics
  • Security Engineering

Background:

  • Face recognition (FR) systems are crucial for identity authentication but vulnerable to spoofing attacks using fake faces.
  • Existing anti-spoofing methods require enhancement for improved reliability in FR systems.

Purpose of the Study:

  • To propose an effective face liveness detection method to defend against spoofing attacks.
  • To enhance the reliability and security of face recognition systems.

Main Methods:

  • A novel method utilizing the defocus effect for face liveness detection is proposed.
  • Feature extraction includes focus, power histogram, and Gradient Location and Orientation Histogram (GLOH) from sequentially captured images.
  • Feature-level fusion is employed for detecting forged faces.

Main Results:

  • The proposed method achieved a 3.29% half total error rate (HTER) under specific depth of field (DoF) conditions.
  • Performance was validated using two distinct databases captured with a digital camera and a webcam.

Conclusions:

  • The defocus-based face liveness detection method offers a robust defense against spoofing attacks.
  • The approach is adaptable for integration into camera-equipped devices, including smartphones, for enhanced security.