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Mobile Face Liveness Detection

The dataset consists of videos featuring individuals wearing various types of masks. Videos are recorded under different lighting conditions and with different attributes (glasses, masks, hats, hoods, wigs, and mustaches for men).

In the dataset, there are 4 types of videos filmed on mobile devices:

  • 2D mask with holes for eyes - demonstration of an attack with a paper/cardboard mask (mask)
  • 2D mask with holes for eyes, nose, and mouth - demonstration of an attack with a paper/cardboard mask with cutouts for the nose and mouth (mask_cut)
  • 2D mask - demonstration of an attack with a paper/cardboard silhouette (outline)
  • Real Video - demonstration of a real person's face (real)

The dataset allows researchers and developers in recognizing and analyzing facial expressions, anti-spoofing tasks, face detection, re-identification and face recognition tasks. The inclusion of various attributes and different lighting conditions aims to enhance the robustness and effectiveness of anti-spoofing models in real-world scenarios.

Full version of the dataset includes 7,200+ videos of people, leave a request on TrainingData to buy the dataset

Statistics for the dataset (gender, type of the device, type of the attack):

Get the Dataset

This is just an example of the data

Leave a request on https://trainingdata.pro/datasets to learn about the price and buy the dataset

Content

The folder files includes:

  • mask - includes videos of people wearing 2D mask with holes for eyes,
  • mask_cut - includes videos of people wearing 2D mask with holes for eyes, nose, and mouth,
  • outline - includes videos of people wearing 2D mask,
  • real - includes real videos of people

File with the extension .csv

  • file: link to access the file,
  • type: type of the video (real, mask, outline, mask_cut)

TrainingData provides high-quality data annotation tailored to your needs

More datasets in TrainingData's Kaggle account: https://www.kaggle.com/trainingdatapro/datasets

TrainingData's GitHub: https://github.com/Trainingdata-datamarket/TrainingData_All_datasets

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