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Commercial
Printed 3D Masks Attacks Dataset
It is a diverse 3D mask attacks dataset containing over 3,800 videos of individuals wearing or holding 3D printed face masks, designed for training facial recognition and liveness detection models, with detailed metadata and realistic mask attacks to support robust anti-spoofing systems and image recognition tasks.
-
- videos
- 3,800+
-
- devices
- 5
- iBeta
- Liveness Detection
- Computer Vision
- Security
- iBeta
- Liveness Detection
- Computer Vision
- Security
It is a diverse 3D mask attacks dataset containing over 3,800 videos of individuals wearing or holding 3D printed face masks, designed for training facial recognition and liveness detection models, with detailed metadata and realistic mask attacks to support robust anti-spoofing systems and image recognition tasks.
- iBeta
- Liveness Detection
- Computer Vision
- Security
-
- videos
- 3,800+
-
- devices
- 5
Dataset Info
| Characteristic | Data |
| Description | Videos of individuals wearing or holding 3D masks |
| Data types | Video |
| Tasks | Liveness Detection, Computer Vision, iBeta |
| Number of video | 3 800+ |
| Labeling | Metadata (age, gender, ethnicity, devices) |
| Gender | Male, Female |
| Number of attributes | 31 |
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Statistics
- Age of the unique people
- Gender distribution
- Ethnicity of the unique people
Technical
Characteristics
| Characteristic | Data |
| Video extension | MP4, MOV |
| Video Resolutions | Min = 1920х1080, Max = 3840х2160 |
| Video duration | 4 second |
| Number of background | 9 |
| Video overlap | No more than 5% |
| Devices | IOS, Android |
Dataset Use Cases
Is it possible to request a custom dataset?
Yes. You can request a customized dataset tailored to your research needs, including specific mask materials, facial angles, lighting conditions, or device types. Unidata can also generate custom 3D mask attack videos with defined demographic attributes for enhanced testing in biometric and security applications.
How was the data collected?
All videos were collected by a verified Unidata partner under standardized conditions using iOS and Android devices. The recording process ensures diversity across backgrounds, resolutions, and camera types while maintaining strict privacy controls and data collection ethics.
How are Unidata datasets licensed?
Unidata datasets follow a dual-licensing model: free samples are available for testing, while full datasets can be purchased for commercial use.
Do Unidata datasets follow GDPR or other data privacy regulations?
Yes. All Unidata datasets are curated in full compliance with GDPR and relevant international data protection laws. The data is collected exclusively from legally authorized sources, ensuring that every video and metadata record adheres to ethical and lawful usage standards.
How are Unidata datasets stored?
All datasets are securely hosted on AWS cloud infrastructure, offering high availability and scalability for large-scale data handling. Unidata’s storage and management practices comply with ISO 27001 and ISO 27701 certifications, providing a secure, privacy-oriented framework for sensitive video and biometric data.
How long does it take to receive the dataset?
After you submit your request, Unidata will contact you to verify details and finalize documentation. Once agreements are signed and payment is confirmed, the dataset is delivered within 3–10 business days.
Is this a real-world dataset or synthetic data?
This is a real-world dataset composed of authentic videos of individuals interacting with 3D-printed face masks. The data was recorded using real devices in controlled environments to simulate realistic mask attacks, making it ideal for testing the resilience of facial recognition technologies.
Why are 3D printed masks more difficult for biometric systems to detect?
Unlike flat printed photos, 3D masks reproduce facial depth and geometry, making them more challenging presentation attacks for facial recognition systems. This dataset helps AI models learn subtle visual cues that distinguish real faces from realistic 3D mask attacks.
Still have questions about using Unidata datasets?
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