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Analysis of Real-Time Face-Verification Methods for Surveillance Applications.

Filiberto Perez-Montes1, Jesus Olivares-Mercado1, Gabriel Sanchez-Perez1

  • 1Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico.

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|February 24, 2023
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Summary
This summary is machine-generated.

This study compares real-time face verification methods for surveillance. EfficientNet-B0 handles general issues, while MobileFaceNet excels with extreme face rotation.

Keywords:
EfficientNetGhostNetMobileFaceNetface verificationlightweight face recognitionvideo surveillance

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Biometrics

Background:

  • Deep learning face recognition uses complex models, limiting deployment on resource-constrained devices.
  • Lightweight methods offer real-time performance but struggle with unconstrained surveillance conditions like low resolution and face rotation.

Purpose of the Study:

  • To compare three state-of-the-art (SOTA) real-time face verification methods for surveillance applications.
  • To evaluate method performance against challenges such as face rotation and low-resolution images.

Main Methods:

  • Created a 3000-image evaluation subset with varied face rotation and resolution levels from existing datasets.
  • Methodically evaluated MobileFaceNet, EfficientNet-B0, and GhostNet on the custom subset and conventional datasets (Cross-Pose LFW, QMUL-SurvFace).

Main Results:

  • EfficientNet-B0 demonstrated capability in handling both low-resolution and face rotation issues common in surveillance.
  • MobileFaceNet showed superior performance in extreme face rotation scenarios, exceeding 80 degrees.

Conclusions:

  • EfficientNet-B0 is a strong candidate for general surveillance face verification.
  • MobileFaceNet is recommended for applications requiring robust performance under severe face rotation conditions.