TrainingDataPro/2d-masks-presentation-attack-detection

---
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](https://trainingdata.pro/datasets/presentation-attack-detection?utm_source=huggingface&utm_medium=cpc&utm_campaign=2d-masks-presentation-attack-detection)** 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.

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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.

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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](https://trainingdata.pro/datasets/presentation-attack-detection?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/datasets/presentation-attack-detection?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>**

*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*

原始信息汇总

数据集概述

数据集名称

2D Masks Presentation Attack Detection - Biometric Attack dataset

数据集内容

该数据集包含以下类型的视频：

• real：4个无面具的人的视频。
• mask：4个佩戴印刷2D面具的人的视频。
• cut：4个佩戴带有眼部切口的印刷2D面具的人的视频。

数据集特征

数据集包含以下特征：

• user：视频中的人物，数据类型为字符串。
• real_1 至 real_4：指向无面具人物视频的链接，数据类型为字符串。
• mask_1 至 mask_4：指向佩戴2D面具人物视频的链接，数据类型为字符串。
• cut_1 至 cut_4：指向佩戴带有眼部切口的2D面具人物视频的链接，数据类型为字符串。

数据集划分

• train：包含17个示例，总大小为4607字节。

数据集用途

该数据集用于计算机视觉、反欺骗任务、视频分析和安全系统的研究，特别是用于开发能够有效检测由佩戴印刷2D面具的个人实施的攻击的算法和模型。

数据集版本

完整版本包含7251个视频，可通过TrainingData购买。