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Liveness Detection Dataset: 3D Paper Mask Attacks
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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.
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