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Private Face Image Generation Method Based on Deidentification in Low Light.

Beibei Dong1, Zhenyu Wang2, Zhihao Gu1

  • 1School of Information Science and Engineering, Hebei North University, Zhangjiakou 075000, China.

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|March 28, 2022
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This study introduces a novel method for generating private, low-light face images by de-identifying facial data. The technique effectively protects user privacy while maintaining image utility, outperforming existing algorithms.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Biometrics

Background:

  • Face recognition technology poses privacy risks by linking facial features to personal information.
  • Existing algorithms struggle with privacy protection in low-light conditions.
  • De-identification is crucial for safeguarding sensitive biometric data.

Purpose of the Study:

  • To develop a method for generating private face images under low-light conditions.
  • To de-identify facial data while preserving image structure and utility.
  • To enhance user privacy against facial recognition systems.

Main Methods:

  • Pretraining light enhancement and attenuation networks.
  • Enhancing low-light images and intercepting facial areas.
  • Generating de-identified latent codes with feature disentanglement.
  • Creating private low-light face images using a face generation network.

Main Results:

  • The proposed method successfully generates low-light private face images.
  • Generated images exhibit high structural similarity to original photos.
  • Effectively reduces face recognition accuracy, enhancing privacy.
  • Maintains image practicability for various applications.

Conclusions:

  • The de-identification method offers effective privacy protection for face images.
  • Achieves superior performance in generating private low-light facial images compared to state-of-the-art methods.
  • Balances privacy preservation with image usability in challenging lighting conditions.