metadata

language:
  - en
license: cc-by-nc-nd-4.0
task_categories:
  - image-classification
tags:
  - code
dataset_info:
  features:
    - name: user
      dtype: string
    - name: real_1
      dtype: string
    - name: real_2
      dtype: string
    - name: real_3
      dtype: string
    - name: real_4
      dtype: string
    - name: mask_1
      dtype: string
    - name: mask_2
      dtype: string
    - name: mask_3
      dtype: string
    - name: mask_4
      dtype: string
    - name: cut_1
      dtype: string
    - name: cut_2
      dtype: string
    - name: cut_3
      dtype: string
    - name: cut_4
      dtype: string
  splits:
    - name: train
      num_bytes: 4607
      num_examples: 17
  download_size: 901061924
  dataset_size: 4607

2D Masks Presentation Attack Detection - Biometric Attack dataset

The anti spoofing dataset consists of videos of individuals wearing printed 2D masks or printed 2D masks with cut-out eyes and directly looking at the camera. Videos are filmed in different lightning conditions and in different places (indoors, outdoors). Each video in the liveness detection dataset has an approximate duration of 2 seconds.

💴 For Commercial Usage: Full version of the dataset includes 7251 videos, leave a request on TrainingData to buy the dataset

Types of videos in the dataset:

real - 4 videos of the person without a mask.
mask - 4 videos of the person wearing a printed 2D mask.
cut - 4 videos of the person wearing a printed 2D mask with cut-out holes for eyes.

People in the dataset wear different accessorieses, such as glasses, caps, scarfs, hats and masks. Most of them are worn over a mask, however glasses and masks can be are also printed on the mask itself.

The dataset serves as a valuable resource for computer vision, anti-spoofing tasks, video analysis, and security systems. It allows for the development of algorithms and models that can effectively detect attacks perpetrated by individuals wearing printed 2D masks.

The dataset comprises videos of genuine facial presentations using various methods, including 2D masks and printed photos, as well as real and spoof faces. It proposes a novel approach that learns and extracts facial features to prevent spoofing attacks, based on deep neural networks and advanced biometric techniques.

Our results show that this technology works effectively in securing most applications and prevents unauthorized access by distinguishing between genuine and spoofed inputs. Additionally, it addresses the challenging task of identifying unseen spoofing cues, making it one of the most effective techniques in the field of anti-spoofing research.

💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/datasets to discuss your requirements, learn about the price and buy the dataset

Content

The folder "files" includes 17 folders:

corresponding to each person in the sample
containing of 12 videos of the individual

File with the extension .csv

user: person in the videos,
real_1,... real_4: links to the videos with people without mask,
mask_1,... mask_4: links to the videos with 2D mask,
cut_1,... cut_4: links to the videos with 2D mask with cut-out eyes

Attacks might be collected in accordance with your requirements.

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

keywords: ibeta level 1, ibeta level 2, liveness detection systems, liveness detection dataset, biometric dataset, biometric data dataset, biometric system attacks, anti-spoofing dataset, face liveness detection, deep learning dataset, face spoofing database, face anti-spoofing, face recognition, face detection, face identification, human video dataset, video dataset, presentation attack detection, presentation attack dataset, 2d print attacks, print 2d attacks dataset, phone attack dataset, face anti spoofing, large-scale face anti spoofing, rich annotations anti spoofing dataset, cut prints spoof attack