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Commercial
2D Masks with Eyeholes Attacks Dataset
This masks with eyeholes dataset contains over 11,200 well-annotated videos of people wearing or holding 2D masks with eyeholes. Designed for facial recognition, fraud prevention, and iBeta Level 1 & 2 certification, it supports training face antispoofing models, spoofing detection algorithms, and liveness detection for real-world biometric security.
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- videos
- 11,200+
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- devices
- 5
- iBeta
- Liveness Detection
- Computer Vision
- Security
- Facial Recognition
- Anti-spoofing
- iBeta
- Liveness Detection
- Computer Vision
- Security
- Facial Recognition
- Anti-spoofing
This masks with eyeholes dataset contains over 11,200 well-annotated videos of people wearing or holding 2D masks with eyeholes. Designed for facial recognition, fraud prevention, and iBeta Level 1 & 2 certification, it supports training face antispoofing models, spoofing detection algorithms, and liveness detection for real-world biometric security.
- iBeta
- Liveness Detection
- Computer Vision
- Security
- Facial Recognition
- Anti-spoofing
-
- videos
- 11,200+
-
- devices
- 5
Dataset Info
| Characteristic | Data |
| Description | Videos of individuals wearing or holding 2D masks with eyeholes, |
| Data types | Video |
| Tasks | Liveness Detection, Computer Vision |
| Total number of files | 11,200+ |
| Labeling | Metadata (age, gender, ethnicity, devices) |
| Gender | Male, Female |
| Number of attributes | 31 |
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Statistics
- Devices in the dataset
- Gender distribution
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
FAQs
What does 2D Masks with Eyeholes Attacks Dataset include?
It contains 11,200+ short video clips of individuals wearing or holding 2D masks with eyeholes. It provides demographic metadata such as age, gender, ethnicity, and device type for each recording.
How was the dataset collected?
Data was gathered through crowdsourcing platforms, using both iOS and Android devices. Videos were recorded in nine different backgrounds, with resolutions ranging from 1920×1080 to 3840×2160 for realistic attack detection scenarios.
How long are the videos?
Each video clip lasts around 4 seconds, making it suitable for training real-time detection algorithms in biometric authentication and liveness detection systems.
Is it possible to request a custom anti-spoofing dataset?
Yes. Unidata can provide custom datasets with different attack types, mask variations, or recording conditions. This flexibility ensures your anti-spoofing systems are trained on the most relevant data.
Do Unidata datasets follow GDPR or other data privacy regulations?
Yes. All Unidata datasets are fully compliant with GDPR and international data protection frameworks. This dataset was collected through legally permissible and ethical sources, containing no personally identifiable information while supporting face anti-spoofing research and biometric security development.
How are Unidata datasets stored?
Unidata securely stores all datasets, including the anti-spoofing mask dataset, on AWS cloud servers. Data management aligns with ISO 27001 and ISO 27701 standards to ensure information security, availability, and privacy.
Is this a real-world dataset or synthetic data?
This is a real-world dataset collected via crowdsourcing platforms. Each video showcases authentic human interactions with printed mask attacks under diverse environments, lighting conditions, and devices, making it highly suitable for training deep learning models in biometric authentication, spoofing prevention, and face recognition security research.
Still have questions about using Unidata datasets?
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