---
license: cc-by-4.0
task_categories:
- image-feature-extraction
- image-classification
- video-classification
language:
- en
tags:
- liveness detection
- anti-spoofing
- biometrics
- facial recognition
- machine learning
- deep learning
- AI
- paper mask attack
- iBeta certification
- PAD attack
- security
- ibeta
- face recognition
- pad
- authentication
- fraud
---
# Liveness Detection Dataset: 3D Paper Mask Attacks

## Full version of the dataset is available for commercial usage. Leave a request on our website [Axonlabs](https://axonlab.ai/?utm_source=hugging-face&utm_medium=cpc&utm_campaign=profile&utm_content=profile_link) to purchase the dataset 💰
## For feedback and additional sample requests, please contact us!

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20109613%2Fc1a66077f517a6165a102023b369e0c1%2FMerged.jpg?generation=1730402472776480&alt=media)

## Dataset Description

The **3D Paper Mask Attack Dataset** focuses on **3D volume-based paper attacks**, incorporating elements such as the nose, shoulders, and forehead. These attacks are designed to be advanced and are useful for both **PAD level 1** and **level 2** liveness tests. This dataset includes videos captured using various mobile devices and incorporates active liveness detection techniques.

## Key Features
- **40+ Participants**: Engaged in the dataset creation, with a balanced representation of Caucasian, Black, and Asian ethnicities.
- **Video Capture**: Videos are captured on both **iOS and Android phones**, with **multiple frames** and **approximately 7 seconds** of video per attack.
- **Active Liveness**: Includes a **zoom-in and zoom-out phase** to simulate active liveness detection.
- **Diverse Scenarios**: 
  - Options to add **volume-based elements** such as scarves, glasses, and hoodies.
  - Captured using both **low-end and high-end devices**.
  - Includes specific **attack scenarios** and **movements**, especially useful for **active liveness testing**.
  - **Specific paper types** are used for attacks, contributing to the diversity of the dataset.

## Ongoing Data Collection
- This dataset is still in the data collection phase, and we welcome feedback and requests to incorporate additional features or specific requirements.

## Potential Use Cases
This dataset is ideal for training and evaluating models for:
- **Liveness Detection**: Distinguishing between selfies and advanced spoofing attacks using 3D paper masks.
- **iBeta Liveness Testing**: Preparing models for **iBeta** liveness testing, ensuring high accuracy in differentiating real faces from spoof attacks.
- **Anti-Spoofing**: Enhancing security in biometric systems by identifying spoof attacks involving paper masks and other advanced methods.
- **Biometric Authentication**: Improving facial recognition systems' resilience to sophisticated paper-based spoofing attacks.
- **Machine Learning and Deep Learning**: Assisting researchers in developing robust liveness detection models for various testing scenarios.

## Keywords
- iBeta Certifications
- PAD Attacks
- Presentation Attack Detection
- Antispoofing
- Liveness Detection
- Spoof Detection
- Facial Recognition
- Biometric Authentication
- Security Systems
- AI Dataset
- 3D Mask Attack Dataset
- Active Liveness
- Anti-Spoofing Technology
- Facial Biometrics
- Machine Learning Dataset
- Deep Learning

## Contact and Feedback
We welcome your feedback! Feel free to reach out to us and share your experience with this dataset. If you're interested, you can also **receive additional samples for free**! 😊

Visit us at [**Axonlabs**](https://axonlab.ai/?utm_source=hugging-face&utm_medium=cpc&utm_campaign=profile&utm_content=profile_link) to request a full version of the dataset for commercial usage.