Idiap Research Institute
Published January 12, 2022 | Version v1
Dataset Restricted

in-Vehicle Face Presentation Attack Detection (VFPAD)

Creators

  • 1. Idiap Research Institute

Description

Description

The in-Vehicle Face Presentation Attack Detection (VFPAD) dataset consists of 4046 bona-fide recordings from 40 subjects, and 1790 attack presentation videos from a total of 89 PAIs (presentation attack instruments). These presentations have been captured using an NIR camera (940 nm) placed on the steering wheel of the car, while NIR illuminators have been fixed on both front pillars (adjacent to the wind-shield) of the car. The bona-fide videos represent 24 male and 16 female subjects of various ethnicities. The PAI species used to construct this dataset include photo-prints, digital displays (for replay attacks), rigid 3D masks, and flexible 3D masks made of silicone.

 

Data Collection

The videos comprising this dataset represent bona-fide and attack presentations under a range of variations:

  • Environmental variations: presentations have been recorded in four sessions, each under different environmental conditions (outdoor sunny; outdoor cloudy; indoor dimly-lit; and indoor brightly-lit)
  • Different scenarios: bona-fide presentations for each subject have been captured with variety of appearances: with/without glasses, with/without hat, etc.
  • Illumination variations: two illumination conditions have been used: ‘uniform’ (both NIR illuminators switched on), and ‘non-uniform’ (only the left NIR-illuminator switched on), and
  • Pose variations: two poses (‘angles’) have been used: ‘front’: the subject looks ahead at the road; and ‘below’: subject looks straight into the camera.

 

Citation

If you use the dataset, please cite the following publication:

@article{IEEE_TBIOM_2021,
  author = {Kotwal, Ketan and Bhattacharjee, Sushil and Abbet, Philip and Mostaani, Zohreh and Wei, Huang and Wenkang, Xu and Yaxi, Zhao and Marcel, S\'{e}bastien},
  title = {Domain-Specific Adaptation of CNN for Detecting Face Presentation Attacks in NIR},
  journal = {IEEE Transactions on Biometrics, Behavior, and Identity Science},
  publisher = {{IEEE}},
  year={2022},
  volume={4},
  number={1},
  pages={135--147},
  doi={10.1109/TBIOM.2022.3143569}
}

Files

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Additional details

Related works

Is documented by
Journal article: 10.1109/TBIOM.2022.3143569 (DOI)