Presentation Attack Detection Methods for Face Recognition Systems: A Comprehensive Survey
Abstract
The vulnerability of face recognition systems to presentation attacks (also known as direct attacks or spoof attacks) has received a great deal of interest from the biometric community. The rapid evolution of face recognition systems into real-time applications has raised new concerns about their ability to resist presentation attacks, particularly in unattended application scenarios such as automated border control. The goal of a presentation attack is to subvert the face recognition system by presenting a facial biometric artifact. Popular face biometric artifacts include a printed photo, the electronic display of a facial photo, replaying video using an electronic display, and 3D face masks. These have demonstrated a high security risk for state-of-the-art face recognition systems. However, several presentation attack detection (PAD) algorithms (also known as countermeasures or antispoofing methods) have been proposed that can automatically detect and mitigate such targeted attacks. The goal of this survey is to present a systematic overview of the existing work on face presentation attack detection that has been carried out. This paper describes the various aspects of face presentation attacks, including different types of face artifacts, state-of-the-art PAD algorithms and an overview of the respective research labs working in this domain, vulnerability assessments and performance evaluation metrics, the outcomes of competitions, the availability of public databases for benchmarking new PAD algorithms in a reproducible manner, and finally a summary of the relevant international standardization in this field. Furthermore, we discuss the open challenges and future work that need to be addressed in this evolving field of biometrics.
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Model fairness can be enforced more directly via optimization techniques, such as using quantitative fairness metrics as regularization or constraints for model parameter optimization.
“Alternatively, model fairness can be enforced more directly via optimization techniques. For instance, quantitative fairness metrics can be used as regularization [6] or constraints for the optimization of the model parameters [66].”
From Trustworthy AI: From Principles to Practices (2023) · cites this paper as [66]
Naive face recognition can be easily spoofed by a photo of a person targeted for identity theft.
“Naive face recognition can be easily spoofed by a photo of a person targeted for identity theft.”
From Trustworthy AI: From Principles to Practices (2023) · cites this paper as [66]
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- Authors
- Ramachandra, R., & Busch, C.
- Affiliations
- Norwegian University of Science and Technology (NO)
- Venue
- ACM Computing Surveys
- Published
- 2017-03-20
- Volume
- 50
- Issue
- 1
- Pages
- 1-37
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2602244503
- Cited by
- 332
- References
- 98
- Citations by year
- 2026: 21 · 2025: 40 · 2024: 29 · 2023: 33 · 2022: 30 · 2021: 56 · 2020: 48 · 2019: 41 · 2018: 23 · 2017: 11
- FWCI
- 28.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Computing Surveys (publishedVersion)
- Funders
- SECUNET Security Network AG, Germany
- Research Council of Norway (IKTPLUSS 248030/O70)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- anti-spoofing, face recognition, spoofing attacks, replay attacks, automated border control, vulnerability assessment
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