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An Identity Authentication Method Combining Liveness Detection and Face Recognition.

Shuhua Liu1, Yu Song2, Mengyu Zhang3

  • 1School of Information Science and Technology, Northeast Normal University, Changchun 130117, China. Liush129@nenu.edu.cn.

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Summary
This summary is machine-generated.

This study introduces a novel liveness detection method using infrared (IR) images from a Kinect sensor to prevent face spoofing. The system achieves 99.8% accuracy, enhancing identity authentication when combined with the FaceNet model.

Keywords:
FaceNetKinect cameradeep learninginfrared radiationliveness detection

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Area of Science:

  • Computer Vision
  • Biometrics
  • Artificial Intelligence

Background:

  • Face spoofing poses a significant threat to identity authentication systems.
  • Existing liveness detection methods often struggle with sophisticated spoofing techniques.

Purpose of the Study:

  • To develop an advanced liveness detection system using infrared (IR) images for robust anti-spoofing.
  • To integrate this liveness detection with the FaceNet model for enhanced identity authentication.

Main Methods:

  • Acquisition of infrared (IR) images using an advanced Kinect sensor.
  • Feature extraction and classification using a deep neural network for distinguishing live faces from spoofs.
  • Integration with the FaceNet model, incorporating two improved methods for enhanced recognition.

Main Results:

  • The proposed IR-based liveness detection achieved a recognition accuracy of 99.8%, outperforming other algorithms.
  • The combined system demonstrated a good recognition effect for identity authentication.
  • The IR images' depth information and hierarchical structure were key for distinguishing live subjects from spoofs.

Conclusions:

  • The advanced Kinect sensor and IR imaging provide an effective solution for liveness detection.
  • The integration of IR-based liveness detection with improved FaceNet significantly enhances identity authentication security.
  • This approach offers a robust and accurate method to combat face spoofing in biometric systems.