Datasets:
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---
language:
- en
license: cc-by-nc-nd-4.0
task_categories:
- video-classification
tags:
- ibeta
- liveness detection
- biometric
- anti-spoofing
dataset_info:
features:
- name: link
dtype: string
- name: type
dtype: string
splits:
- name: train
num_bytes: 1452
num_examples: 48
download_size: 737352851
dataset_size: 1452
size_categories:
- 10K<n<100K
---
# 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.
## 👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset - [Full dataset](https://unidata.pro/datasets/2d-printed-mask-and-replay-attack-videos-dataset/?utm_source=huggingface-td&utm_medium=referral&utm_campaign=cut-2d-masks-presentation-attack-detection)
### 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.
## 🧩 This is just an example of the data. Leave a request [here](https://unidata.pro/datasets/2d-printed-mask-and-replay-attack-videos-dataset/?utm_source=huggingface-td&utm_medium=referral&utm_campaign=cut-2d-masks-presentation-attack-detection) to learn more
# 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.
**🚀 You can learn more about our high-quality unique datasets [here](https://unidata.pro/datasets/2d-printed-mask-and-replay-attack-videos-dataset/?utm_source=huggingface-td&utm_medium=referral&utm_campaign=cut-2d-masks-presentation-attack-detection)** |