The viewer is disabled because this dataset repo requires arbitrary Python code execution. Please consider
removing the
loading script
and relying on
automated data support
(you can use
convert_to_parquet
from the datasets library). If this is not possible, please
open a discussion
for direct help.
Cut 2D Masks Presentation Attack Detection - Biometric Attack dataset
The anti spoofing dataset consists of videos of individuals wearing printed 2D masks with cut-out holes for eyes, noses and mouths. Videos are filmed in different lightning conditions and in different places (indoors, outdoors), a person moves his/her head left, right, up and down. Each video in the liveness detection dataset has an approximate duration of 7 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:
- 2d_mask - videos of the person wearing a printed 2D mask with cut-out holes for eyes.
- cut_mask - videos of the person wearing a printed 2D mask with cut-out holes for eyes, mouth and nose. All videos represent masks with holes for eyes, in some videos holes for both mouth and nose are made, in others only for mouth or nose.
.png?generation=1690468363734380&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.
.png?generation=1690468790515642&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.
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 dataset contains of two folders:
- 2d_masks contains videos of the person wearing a printed 2D mask with cut-out holes for eyes.
- cut_masks includes videos of the person wearing a printed 2D mask with cut-out holes for eyes, mouth and nose.
File with the extension .csv
- link: link to access the video,
- type: type of the attack: with printed 2D mask with cut-out holes for eyes OR with printed 2D mask with cut-out holes for eyes, mouth and nose
Attacks might be collected in accordance with your requirements.
TrainingData provides high-quality data annotation tailored to your needs
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
More datasets in TrainingData's Kaggle account: https://www.kaggle.com/trainingdatapro/datasets
TrainingData's GitHub: https://github.com/Trainingdata-datamarket/TrainingData_All_datasets
- Downloads last month
- 0