Update README.md

README.md

@@ -1,3 +1,66 @@
----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+tags:
+  - "liveness detection"
+  - "anti-spoofing"
+  - "biometrics"
+  - "facial recognition"
+  - "machine learning"
+  - "deep learning"
+  - "AI"
+  - "paper mask attack"
+  - "iBeta certification"
+  - "PAD attack"
+  - "security"
+---
+# Liveness Detection Dataset: iBeta Level 1 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 **iBeta Level 1 Certification Dataset** focuses on **paper mask attacks** tested during **iBeta Level 1** **Presentation Attack Detection (PAD)**. This dataset includes multiple variations of paper mask attacks for training AI models to distinguish between real and spoofed facial data, and it is tailored to meet the requirements for iBeta certifications.
+
+### 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 **iOS and Android phones**, featuring **multiple frames** and **approximately 10 seconds** of video per attack.
+- **18,000+ Paper Mask Attacks**: Including a variety of attack types such as print and cutout paper masks, cylinder-based attacks to create a volume effect, and 3D masks with volume-based elements (e.g., nose).
+- **Active Liveness Testing**: Includes a **zoom-in and zoom-out phase** to simulate **active liveness detection**.
+- **Variation in Attacks**:
+  - Real-life selfies and videos from participants.
+  - **Print and Cutout Paper Attacks**.
+  - **Cylinder Attacks** to simulate volume effects.
+  - **3D Paper Masks** with additional volume elements like the nose and other facial features.
+  - Paper attacks on actors with **head and eye variations**.
+  
+### Potential Use Cases
+This dataset is ideal for training and evaluating models for:
+- **Liveness Detection**: Enabling researchers to distinguish between selfies and spoof attacks with high accuracy.
+- **iBeta Liveness Testing**: Preparing models for **iBeta** liveness testing, which requires precise spoof detection to meet certification standards.
+- **Anti-Spoofing**: Enhancing security in biometric systems by improving detection of paper mask spoofing techniques.
+- **Biometric Authentication**: Strengthening facial recognition systems to detect a variety of spoofing attempts.
+- **Machine Learning and Deep Learning**: Assisting researchers in developing robust liveness detection models for real-world applications.
+
+### Keywords
+- iBeta Certifications
+- PAD Attacks
+- Presentation Attack Detection
+- Antispoofing
+- Liveness Detection
+- Spoof Detection
+- Facial Recognition
+- Biometric Authentication
+- Security Systems
+- AI Dataset
+- Paper Mask Attack Dataset
+- 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.
+