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Presentation-level Privacy Protection Techniques for Automated Face Recognition—A Survey

Authors:

Md Rezwan Hasan,

Farzin DeraviAuthors Info & Claims

ACM Computing Surveys, Volume 55, Issue 13s

Article No.: 286, Pages 1 - 27

https://doi.org/10.1145/3583135

Published: 13 July 2023 Publication History

Abstract

The use of Biometric Facial Recognition (FR) systems have become increasingly widespread, especially since the advent of deep neural network-based architectures. Although FR systems provide substantial benefits in terms of security and safety, the use of these systems also raises significant privacy concerns. This article discusses recent advances in facial identity hiding techniques, focusing on privacy protection approaches that hide or protect facial biometric data before camera devices capture the data. Moreover, we also discuss the state-of-the-art methods used to evaluate such privacy protection techniques. The primary motivation of this survey is to assess the relative performance of facial privacy protection methods and identify open challenges and future work that needs to be considered in this research area.

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Index Terms

Presentation-level Privacy Protection Techniques for Automated Face Recognition—A Survey
1. Security and privacy
  1. Human and societal aspects of security and privacy

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 13s

December 2023

1367 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3606252

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 13 July 2023

Online AM: 09 February 2023

Accepted: 30 January 2023

Revised: 24 January 2023

Received: 09 February 2022

Published in CSUR Volume 55, Issue 13s

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Jeyaraj RSubramanian BYogesh KJin AGohel H(2024)YSAF: Yolo with Spatial Attention and FFT to Detect Face Spoofing Attacks2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC)10.1109/ICAIC60265.2024.10433802(1-6)Online publication date: 7-Feb-2024
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