Face Biometrics Under Spoofing Attacks: Vulnerabilities, Countermeasures, Open Issues, and Research Directions

@article{Hadid2014FaceBU,
  title={Face Biometrics Under Spoofing Attacks: Vulnerabilities, Countermeasures, Open Issues, and Research Directions},
  author={Abdenour Hadid},
  journal={2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops},
  year={2014},
  pages={113-118},
  url={https://api.semanticscholar.org/CorpusID:9540938}
}
  • A. Hadid
  • Published in IEEE Conference on Computer… 23 June 2014
  • Computer Science
  • 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops
The goal of this position paper is to share the lessons learned about spoofing and anti-spoofing in face biometrics, and to highlight open issues and future directions.
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27 References

On the effectiveness of local binary patterns in face anti-spoofing

This paper inspects the potential of texture features based on Local Binary Patterns (LBP) and their variations on three types of attacks: printed photographs, and photos and videos displayed on electronic screens of different sizes and concludes that LBP show moderate discriminability when confronted with a wide set of attack types.

Motion-based counter-measures to photo attacks in face recognition

A publicly available PHOTO-ATTACK database is introduced, and a new technique of counter-measure solely based on foreground/background motion correlation using Optical Flow that outperforms all other algorithms achieving nearly perfect scoring with an equal-error rate of 1.52% is proposed.

Anti-spoofing in Action: Joint Operation with a Verification System

Techniques for decision- level and score-level fusion to integrate a recognition and anti-spoofing systems are studied, using an open-source framework that handles the ternary classification problem (clients, impostors and attacks) transparently.

Liveness Detection for Face Recognition

A real-time liveness detection approach is presented against photograph spoofing in a non-intrusive manner for face recognition, which does not require any additional hardware except for a generic webcamera.

Face spoofing detection through partial least squares and low-level descriptors

This paper describes an anti-spoofing solution based on a set of low-level feature descriptors capable of distinguishing between ‘live’ and ‘spoof’ images and videos, and explores both spatial and temporal information to learn distinctive characteristics between the two classes.

A face antispoofing database with diverse attacks

A face antispoofing database which covers a diverse range of potential attack variations, and a baseline algorithm is given for comparison, which explores the high frequency information in the facial region to determine the liveness.

Counter-measures to photo attacks in face recognition: A public database and a baseline

This work introduces the publicly available PRINT-ATTACK database and exemplifies how to use its companion protocol with a motion-based algorithm that detects correlations between the person's head movements and the scene context to compare to other counter-measure techniques.

Spoofing in 2D face recognition with 3D masks and anti-spoofing with Kinect

The 3D Mask Attack Database (3DMAD), the first publicly available 3D spoofing database, recorded with a low-cost depth camera is introduced and it is shown that easily attainable facial masks can pose a serious threat to 2D face recognition systems and LBP is a powerful weapon to eliminate it.

Verifying liveness by multiple experts in face biometrics

A holistic liveness detection paradigm that collaborates with standard techniques in 2D face biometrics is suggested that shows that many attacks are avertible via a combination of anti-spoofing measures.

Non-intrusive liveness detection by face images