task_categories:image-classification language:en license:cc-by-nc-nd-4.0 code region:us
The 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 dataset has an approximate duration of 2
seconds.
@InProceedings{huggingface:dataset,
title = {2d-masks-presentation-attack-detection},
author = {TrainingDataPro},
year = {2023}
}
---
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

The 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 dataset has an approximate duration of 2 seconds.

### 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.

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fd29be8e22b3376efc1260f0a90f66d5c%2FMacBook%20Air%20-%201%20(2).png?generation=1690460078319549&alt=media)

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.

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Faa17e51fbcb74d5920dd0f5331f89668%2FMacBook%20Air%20-%201%20(3).png?generation=1690462300531653&alt=media)

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.

Studying the dataset may lead to the development of improved security systems, surveillance technologies, and solutions to mitigate the risks associated with masked individuals carrying out attacks.

# Get the dataset

### This is just an example of the data

Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=2d-masks-presentation-attack-detection) 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**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=2d-masks-presentation-attack-detection) 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>**
