Face Anti-spoofing, Face Presentation Attack Detection

Yaojie Liu, Joel Stehouwer, Amin Jourabloo, Yousef Atoum, Xiaoming Liu

Keywords: Face Antispoofing, Biometrics, Low-level Vision, Database

Biometrics utilize physiological, such as fingerprint, face, and iris, or behavioral characteristics, such as typing rhythm and gait, to uniquely identify or authenticate an individual. As biometric systems are widely used in real-world applications including mobile phone authentication and access control, biometric spoof, or Presentation Attack (PA) are becoming a larger threat, where a spoofed biometric sample is presented to the biometric system and attempted to be authenticated. Since face is the most accessible biometric modality, there have been many different types of PAs for faces including print attack, replay attack, 3D masks, etc. As a result, conventional face recognition systems can be very vulnerable to such PAs. We proposes a novel two-stream CNN-based face antispoofing method, for print and replay attacks. The proposed method extracts the local features and holistic depth maps from face images.

Overview Face Recon

Figure 1: Architecture of the proposed face anti-spoofing approach.

PC Demo

Mobile Demo

Acknowledgment

This research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA R&D Contract No. 2017-17020200004. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.

GOSet: Generic Object Anti-Spoofing Dataset

Biometric recognition is increasingly used in society for commercial applications and high-level security. However, recent works are not generalizable between the biometric modalities. We collect the GOSet dataset to study content-independent anti-spoofing.

The GOSet dataset is available here.

Face Anti-Spoofing Source Code

Deep Tree Network implementation may be downloaded from here.

Face De-Spoofing implementation may be downloaded from here.

STDN(On Disentangling Spoof Traces for Generic Face Anti-Spoofing) implementation may be downloaded from here.

If you use MSU PAD code, please cite to the papers:

Publications

  • Spoof Trace Disentanglement for Generic Face Anti-Spoofing
    Yaojie Liu, Xiaoming Liu
    IEEE Transactions on Pattern Analysis and Machine Intelligence, , May. 2022
    Bibtex | PDF | arXiv
    • @article{ spoof-trace-disentanglement-for-generic-face-anti-spoofing,
      author = { Yaojie Liu and Xiaoming Liu },
      title = { Spoof Trace Disentanglement for Generic Face Anti-Spoofing },
      journal = { IEEE Transactions on Pattern Analysis and Machine Intelligence },
      month = { May },
      year = { 2022 },
    }
  • On Disentangling Spoof Traces for Generic Face Anti-Spoofing
    Yaojie Liu, Joel Stehouwer, Xiaoming Liu
    In Proceeding of European Conference on Computer Vision (ECCV 2020), Virtual, Aug. 2020
    Bibtex | PDF | arXiv | Code | Video
    • @inproceedings{ on-disentangling-spoof-traces-for-generic-face-anti-spoofing,
      author = { Yaojie Liu and Joel Stehouwer and Xiaoming Liu },
      title = { On Disentangling Spoof Traces for Generic Face Anti-Spoofing },
      booktitle = { In Proceeding of European Conference on Computer Vision },
      address = { Virtual },
      month = { August },
      year = { 2020 },
    }
  • Noise Modeling, Synthesis and Classification for Generic Object Anti-Spoofing
    Joel Stehouwer, Amin Jourabloo, Yaojie Liu, Xiaoming Liu
    In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2020), Seattle, WA, Jun. 2020
    Bibtex | PDF | arXiv | Poster | Code
    • @inproceedings{ noise-modeling-synthesis-and-classification-for-generic-object-anti-spoofing,
      author = { Joel Stehouwer and Amin Jourabloo and Yaojie Liu and Xiaoming Liu },
      title = { Noise Modeling, Synthesis and Classification for Generic Object Anti-Spoofing },
      booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
      address = { Seattle, WA },
      month = { June },
      year = { 2020 },
    }
  • Presentation Attack Detection for Face in Mobile Phones
    Yaojie Liu, Joel Stehouwer, Amin Jourabloo, Yousef Atoum, Xiaoming Liu
    Selfie Biometrics, Ajita Rattani, Reza Derakhshani and Arun Ross, Eds., Springer, 2019
    Bibtex | PDF
    • @incollection{ presentation-attack-detection-for-face-in-mobile-phones,
      author = { Yaojie Liu and Joel Stehouwer and Amin Jourabloo and Yousef Atoum and Xiaoming Liu },
      title = { Presentation Attack Detection for Face in Mobile Phones },
      in book chapter of = { Selfie Biometrics },
      publisher = { Springer },
      editor = { Ajita Rattani, Reza Derakhshani and Arun Ross },
      year = { 2019 },
    }
  • Deep Tree Learning for Zero-shot Face Anti-Spoofing
    Yaojie Liu, Joel Stehouwer, Amin Jourabloo, Xiaoming Liu
    In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2019), Long Beach, CA, Jun. 2019 (Oral Presentation, Best Paper Award Finalist)
    Bibtex | PDF | arXiv | Poster | Code | Video
    • @inproceedings{ deep-tree-learning-for-zero-shot-face-anti-spoofing,
      author = { Yaojie Liu and Joel Stehouwer and Amin Jourabloo and Xiaoming Liu },
      title = { Deep Tree Learning for Zero-shot Face Anti-Spoofing },
      booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
      address = { Long Beach, CA },
      month = { June },
      year = { 2019 },
    }
  • Face De-Spoofing: Anti-Spoofing via Noise Modeling
    Amin Jourabloo*, Yaojie Liu*, Xiaoming Liu
    Proc. European Conference on Computer Vision (ECCV 2018), Munich, Germany, Sep. 2018
    Bibtex | PDF | arXiv | Poster | Code
    • @inproceedings{ face-de-spoofing-anti-spoofing-via-noise-modeling,
      author = { Amin Jourabloo* and Yaojie Liu* and Xiaoming Liu },
      title = { Face De-Spoofing: Anti-Spoofing via Noise Modeling },
      booktitle = { Proc. European Conference on Computer Vision },
      address = { Munich, Germany },
      month = { September },
      year = { 2018 },
    }
  • Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision
    Yaojie Liu*, Amin Jourabloo*, Xiaoming Liu
    In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, UT, Jun. 2018
    Bibtex | PDF | arXiv | Poster | Code
    • @inproceedings{ learning-deep-models-for-face-anti-spoofing-binary-or-auxiliary-supervision,
      author = { Yaojie Liu* and Amin Jourabloo* and Xiaoming Liu },
      title = { Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision },
      booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
      address = { Salt Lake City, UT },
      month = { June },
      year = { 2018 },
    }
  • Face Anti-Spoofing Using Patch and Depth-based CNNs
    Yousef Atoum, Yaojie Liu, Amin Jourabloo, Xiaoming Liu
    In Proceeding of International Joint Conference on Biometrics (IJCB 2017), Denver, CO, Oct. 2017
    Bibtex | PDF | Poster
    • @inproceedings{ face-anti-spoofing-using-patch-and-depth-based-cnns,
      author = { Yousef Atoum and Yaojie Liu and Amin Jourabloo and Xiaoming Liu },
      title = { Face Anti-Spoofing Using Patch and Depth-based CNNs },
      booktitle = { In Proceeding of International Joint Conference on Biometrics },
      address = { Denver, CO },
      month = { October },
      year = { 2017 },
    }