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Encrypted face recognition algorithm based on Ridgelet-DCT transform and THM chaos.

Zilong Liu1,2, Jingbing Li1, Jing Liu3

  • 1School of Information and Communication Engineering, Hainan University, Haikou 570228, China.

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|February 9, 2022
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Summary
This summary is machine-generated.

A novel tent-Henon map (THM) chaotic encryption algorithm enhances face recognition security by encrypting visual features before neural network analysis. This method protects personal privacy in the age of widespread facial data online.

Keywords:
encrypted faceface recognitionneural networkridgelet transformtent-Henon-map

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Area of Science:

  • Computer Science
  • Cryptography
  • Image Processing

Background:

  • The proliferation of facial recognition technology raises significant personal privacy concerns due to widespread internet data.
  • Existing methods often lack robust privacy safeguards for sensitive facial image data.

Purpose of the Study:

  • To develop a secure and robust face recognition algorithm that addresses privacy risks associated with online facial data.
  • To introduce a novel encryption method for face images that preserves essential features for recognition.

Main Methods:

  • A tent-Henon map (THM) chaotic encryption algorithm is proposed, utilizing Ridgelet-DCT transform for feature extraction.
  • Homomorphic encryption is employed to extract visually robust features from face images.
  • A neural network model is designed for the encrypted face recognition task.

Main Results:

  • The algorithm demonstrates effective encryption with good security and robustness.
  • Experimental validation using the ORL face database confirms the algorithm's performance.
  • The method successfully balances encryption with the preservation of recognition capabilities.

Conclusions:

  • The proposed THM chaotic encrypted face algorithm offers a promising solution for secure face recognition.
  • This approach enhances personal privacy protection in the context of increasing facial data usage.
  • The algorithm shows potential for broad applications in secure identification systems.