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Driver Face Verification with Depth Maps.

Guido Borghi1, Stefano Pini2, Roberto Vezzani2

  • 1Softech-ICT, Dipartimento di Ingegneria Enzo Ferrari, Università degli studi di Modena e Reggio Emilia, 41125 Modena, Italy. guido.borghi@unimore.it.

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Summary
This summary is machine-generated.

This study introduces a novel fully-convolutional Siamese network for face verification using depth maps. The system achieves state-of-the-art performance, even with occlusions and in low-light conditions, making it suitable for automotive applications.

Keywords:
Siamese modelautomotivedeep learningdepth mapsdriver face verificationfully-convolutional network

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Area of Science:

  • Computer Vision
  • Biometrics
  • Machine Learning

Background:

  • Face verification is crucial for security and identification.
  • Traditional methods struggle with varying illumination and occlusions.
  • Depth data offers robustness against illumination changes.

Purpose of the Study:

  • To develop a robust and efficient face verification system.
  • To leverage depth maps for improved accuracy and reliability.
  • To achieve state-of-the-art results on challenging datasets.

Main Methods:

  • A fully-convolutional Siamese network architecture was proposed.
  • Depth maps were used as input, enhancing robustness to illumination.
  • The method was trained on limited depth data and optimized for real-time performance on CPUs and embedded systems.

Main Results:

  • State-of-the-art results were achieved on Pandora, HRRFaceD, and CurtinFaces datasets.
  • The system demonstrated acceptable accuracy for real-world automotive applications.
  • High efficacy was shown in handling occluded faces and extreme head poses.

Conclusions:

  • The proposed depth-based Siamese network offers a reliable solution for face verification.
  • Its efficiency and robustness make it suitable for in-vehicle systems.
  • The method effectively addresses challenges like low-light conditions and occlusions.