Resources¶

This section contains a listing of all ready-to-use resources you can find in this package.

Databases¶

These configuration files/resources contain parameters of available databases. The configuration files contain at least the following arguments of the bob pad run-pipeline command:

database

protocol

groups

Replay-Attack Database¶

Replay-Attack is a database for face PAD experiments.

The Replay-Attack Database for face spoofing consists of 1300 video clips of photo and video attack attempts to 50 clients, under different lighting conditions. This Database was produced at the Idiap Research Institute, in Switzerland. The reference citation is [CAM12].

You can download the raw data of the Replay-Attack database by following the link. After downloading, you can tell the bob library where the files are located using:

$ bob config set bob.db.replay_attack.directory /path/to/database/replay/protocols/replayattack-database/

Replay-Mobile Database¶

Replay-Mobile is a database for face PAD experiments.

The Replay-Mobile Database for face spoofing consists of 1030 video clips of photo and video attack attempts to 40 clients, under different lighting conditions. These videos were recorded with current devices from the market – an iPad Mini2 (running iOS) and a LG-G4 smartphone (running Android). This Database was produced at the Idiap Research Institute (Switzerland) within the framework of collaboration with Galician Research and Development Center in Advanced Telecommunications - Gradiant (Spain). The reference citation is [CBVM16].

You can download the raw data of the Replay-Mobile database by following the link. After downloading, you can tell the bob library where the files are located using:

$ bob config set bob.db.replay_mobile.directory /path/to/database/replay-mobile/database/

OULU-NPU Database¶

The OULU-NPU Database. A mobile face presentation attack database with real-world variations database.

To configure the location of the database on your computer, run:

bob config set bob.db.oulu_npu.directory /path/to/database/oulu-npu

If you use this database, please cite the following publication:

@INPROCEEDINGS{OULU_NPU_2017,
         author = {Boulkenafet, Z. and Komulainen, J. and Li, Lei. and Feng, X. and Hadid, A.},
       keywords = {biometrics, face recognition, anti-spoofing, presentation attack, generalization, colour texture},
          month = May,
          title = {{OULU-NPU}: A mobile face presentation attack database with real-world variations},
        journal = {IEEE International Conference on Automatic Face and Gesture Recognition},
           year = {2017},
}

SWAN Database¶

The Swan Database.

To configure the location of the database on your computer, run:

bob config set bob.db.swan.directory /path/to/database/swan

The Idiap part of the dataset comprises 150 subjects that are captured in six different sessions reflecting real-life scenarios of smartphone assisted authentication. One of the unique features of this dataset is that it is collected in four different geographic locations representing a diverse population and ethnicity. Additionally, it also contains a multimodal Presentation Attack (PA) or spoofing dataset using low-cost Presentation Attack Instruments (PAI) such as print and electronic display attacks. The novel acquisition protocols and the diversity of the data subjects collected from different geographic locations will allow developing a novel algorithm for either unimodal or multimodal biometrics.

PAD protocols are created according to the SWAN-PAD-protocols document. Bona-fide session 2 data is split into 3 sets of training, development, and evaluation. The bona-fide data from sessions 3,4,5,6 are used for evaluation as well. PA samples are randomly split into 3 sets of training, development, and evaluation. All the random splits are done 10 times to created 10 different protocols. The PAD protocols contain only one type of attacks. For convenience, PA_F and PA_V protocols are created for face and voice, respectively which contain all the attacks.

Deep Pixel-wise Binary Supervision for Face PAD¶

Deep Pixel-wise Binary Supervision for Face PAD

This baseline includes the models to replicate the experimental results published in the following publication:

@INPROCEEDINGS{GeorgeICB2019,
    author = {Anjith George, Sebastien Marcel},
    title = {Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection},
    year = {2019},
    booktitle = {ICB 2019},
}

Available face PAD systems¶

These configuration files/resources contain parameters of available face PAD systems/algorithms. The configuration files contain at least the following arguments of the bob pad run-pipeline command:

pipeline containing zero, one, or more Transformers and one Classifier