Datasets:
license: cc-by-nc-4.0
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
task_categories:
- video-classification
iBeta Level 2 PAD Anti-Spoofing (3D Masks) — Liveness Detection Training Dataset
Comprehensive biometric dataset for iBeta Level 2 liveness detection training and anti-spoofing research. Emphasis on 3D attacks (masks) and movements for Active liveness (zoom-in/zoom-out, micro-movements), high variability of devices and conditions, high diversity of subjects
Spoofing Attack Types:
- Silicone Mask Attacks
- Latex Mask Attacks
- Wrapped 3D Paper Mask Attacks
- Advanced Paper Mask Attacks
- Cloth 3D Face Mask Attacks
These reflect spoofing attack types commonly explored in iBeta PAD Level 2 test plans
Dataset Description:
- 25,000+ videos (~10 sec), multiframe
- Each attack is captured on iOS and Android phone
- Zoom in and zoom out phase for Active Liveness
Full version of dataset is availible for commercial usage - leave a request on our website Axonlabs to purchase the dataset 💰
Potential Use Cases:
Liveness detection: This dataset is ideal for training and evaluating liveness detection models, enabling researchers to distinguish between selfies and spoof attacks with high accuracy
iBeta liveness testing: This dataset is valuable for training and evaluating liveness detection models before applying to iBeta certifications, enabling researchers to distinguish between selfies and spoof attacks with high accuracy
##Technical Specifications • File Format: Videos are formatted to be compatible with mainstream ML frameworks
• Resolution and Frame Rate: Tailored for high-resolution and optimal frame rates to capture quick mask placements
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