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Commercial
Latex Mask Attacks Dataset
This is a high-resolution collection of 11,100 videos of people wearing latex masks, created for training facial recognition and spoofing detection models. Featuring diverse facial features, metadata, and realistic presentation attacks, it supports fraud prevention research and iBeta Level 2 certification for robust biometric security systems.
-
- videos
- 11,100+
-
- devices
- 5
- iBeta
- Liveness Detection
- Computer Vision
- Security
- Facial Recognition
- Anti-spoofing
- iBeta
- Liveness Detection
- Computer Vision
- Security
- Facial Recognition
- Anti-spoofing
This is a high-resolution collection of 11,100 videos of people wearing latex masks, created for training facial recognition and spoofing detection models. Featuring diverse facial features, metadata, and realistic presentation attacks, it supports fraud prevention research and iBeta Level 2 certification for robust biometric security systems.
- iBeta
- Liveness Detection
- Computer Vision
- Security
- Facial Recognition
- Anti-spoofing
-
- videos
- 11,100+
-
- devices
- 5
Dataset Info
| Characteristic | Data |
| Description | Video of people in latex masks training algorithms to detect biometric hacking attempts. |
| Data types | Video |
| Tasks | Face recognition, Computer Vision |
| Total number of files | 11,100 |
| Labeling | Only technical characteristics and metadata (age, gender, ethnicity) |
| 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 |
| Video Resolutions | Min = 1920х1080, Max = 3840х2160 |
| Video duration | 1-2 second |
| Number of background | 9 |
| Video overlap | No more than 5% |
| Devices | IOS, Android |
Dataset Use Cases
FAQs
What types of annotations are provided?
The dataset provides metadata annotations, including demographic details and technical recording attributes. These labels help train deep learning models for identifying real faces vs. latex masks.
How was the dataset collected?
Videos were captured with binocular RGB and infrared cameras on iOS and Android devices. Recordings span nine different backgrounds with resolutions ranging from 1920×1080 to 3840×2160, ensuring robust training data for detection algorithms.
What is the average duration and quality of videos?
Each video is 1–2 seconds long, designed for quick facial recognition testing. The dataset includes high-resolution footage suitable for object detection, image classification, and biometric attack research.
What types of attacks are included in Latex Mask Attacks Dataset?
The dataset focuses on latex mask presentation attacks, where individuals wear realistic facial masks to imitate another person’s identity. These attack scenarios are valuable for evaluating liveness detection, presentation attack detection (PAD), and face recognition algorithms under real-world biometric threats.
How does this dataset differ from other spoofing datasets?
Unlike datasets focusing on printed photos or replay attacks, this collection highlights latex masks and 3D presentation attacks. It provides more advanced spoofing scenarios, making it essential for next-generation liveness detection systems.
How are Unidata datasets licensed?
Unidata datasets follow a dual-licensing model. Free dataset samples are provided for research trials and algorithm testing, while full, high-resolution datasets - including Latex Mask Attacks Dataset - are available exclusively through purchase.
Do Unidata datasets follow GDPR or other data privacy regulations?
Yes. All Unidata datasets are curated in full compliance with GDPR and international data protection regulations.
How long does it take to receive the dataset?
Once your request is submitted, our team will review the details, prepare the necessary documentation, and provide an agreement for signature. After payment confirmation, the dataset will be delivered within 3 to 10 business days via secure digital transfer.
Is this a real-world dataset or synthetic data?
Latex Mask Attacks Dataset consists of real-world video recordings collected through crowdsourcing platforms, not synthetic data. Each clip captures realistic mask attack scenarios under different environments and devices, making it valuable for developing robust facial recognition and biometric attack detection algorithms.
Still have questions about using Unidata datasets?
Read our user-guides
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