Datasets:

AxonData
/

ibeta-level-1-certification

@@ -29,7 +29,7 @@ task_categories:
 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
 - **80+ 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.
 - **22,000+ 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).
@@ -41,7 +41,7 @@ The **iBeta Level 1 Certification Dataset** focuses on **paper mask attacks** te
   - **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.
@@ -49,7 +49,7 @@ This dataset is ideal for training and evaluating models for:
 - **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

 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
 - **80+ 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.
 - **22,000+ 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).
   - **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.
 - **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