---
language:
- en
license: cc-by-nc-nd-4.0
task_categories:
- video-classification
tags:
- code
- ibeta
- liveness detection
- silicone mask
- deep learning
- biometric
dataset_info:
  features:
  - name: id
    dtype: int32
  - name: name
    dtype: string
  - name: video
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': real
          '1': silicone
          '2': mask
  splits:
  - name: train
    num_bytes: 2394
    num_examples: 62
  download_size: 156861504
  dataset_size: 2394
size_categories:
- 1K<n<10K
---
# Silicone Masks Biometric Attack Dataset
The anti spoofing dataset consists of videos of individuals and attacks with printed 2D masks and silicone masks . Videos are filmed in different lightning conditions (*in a dark room, daylight, light room and nightlight*). Dataset includes videos of people with different attributes (*glasses, mask, hat, hood, wigs and mustaches for men*).

The dataset comprises videos of genuine facial presentations using various methods, including 3D masks and 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.

##  👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset - [Full dataset](https://unidata.pro/datasets/silicone-mask-attacks/?utm_source=huggingface-td&utm_medium=referral&utm_campaign=silicone-masks)
### Types of videos in the dataset:
- **real** - real video of the person 
- **outline** -video of the person wearing a printed 2D mask
- **silicone** - video of the person wearing a silicone mask

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Ff9be1f70a38085709c85716b212cdd11%2FFrame%2027.png?generation=1696329340111093&alt=media)

### Types and number of videos in the full dataset:
- **2885** real videos of people
- **2859** videos of people wearing silicone mask
- **48** videos of people wearing a 2D mask.

### Gender of people in the dataset:
- women: **2685**
- men: **3107**

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.

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*.

## 🧩 This is just an example of the data. Leave a request [here](https://unidata.pro/datasets/silicone-mask-attacks/?utm_source=huggingface-td&utm_medium=referral&utm_campaign=silicone-masks) to learn more

# Content
- **real** - contains of real videos of people,
- **mask** - contains of videos with people wearing a printed 2D mask,
- **silicone** - contains of videos with people wearing a silicone mask,
-  **dataset_info.csvl** - includes the information about videos in the dataset

### File with the extension .csv
- **video**: link to the video,
- **type**: type of the video

# Attacks might be collected in accordance with your requirements.

**🚀 You can learn more about our high-quality unique datasets [here](https://unidata.pro/datasets/silicone-mask-attacks/?utm_source=huggingface-td&utm_medium=referral&utm_campaign=silicone-masks)**

*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, silicone masks attacks, spoofing deep face recognition, phone attack dataset, face anti spoofing, large-scale face anti spoofing, rich annotations anti spoofing dataset*