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README.md

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-license: cc-by-4.0
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+---
+license: cc-by-4.0
+tags:
+  - "liveness detection"
+  - "anti-spoofing"
+  - "biometrics"
+  - "facial recognition"
+  - "machine learning"
+  - "deep learning"
+  - "AI"
+  - "3D mask attack"
+  - "PAD attack"
+  - "active liveness"
+  - "security"
+---
+# Liveness Detection Dataset: 3D Paper Mask Attacks
+
+## The full version of the dataset is available for commercial use. Request access via our website at Axonlabs to purchase the dataset 💰
+## For feedback and additional sample requests, please contact us!
+
+## 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** to request a full version of the dataset for commercial usage.