Commercial

Fabric Masks and Disguise Presentation Attack Dataset

Fabric Masks and Disguise Presentation Attack Dataset is a high-quality face anti-spoofing dataset containing 3,600 HD and 4K videos of people wearing fabric and cloth face masks. Designed for presentation attack detection and face recognition research, this face mask detection dataset includes varied lighting, camera types, and demographic diversity to support robust mask detection and biometric security model training.

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  • Videos
    3,600
  • people
    30
  • Facial Recognition
  • iBeta
  • Liveness Detection
  • Security
  • Anti-spoofing
  • Computer Vision

Fabric Masks and Disguise Presentation Attack Dataset is a high-quality face anti-spoofing dataset containing 3,600 HD and 4K videos of people wearing fabric and cloth face masks. Designed for presentation attack detection and face recognition research, this face mask detection dataset includes varied lighting, camera types, and demographic diversity to support robust mask detection and biometric security model training.

Get in touch Download sample
  • Facial Recognition
  • iBeta
  • Liveness Detection
  • Security
  • Anti-spoofing
  • Computer Vision
  • Videos
    3,600
  • people
    30

Dataset Info

Characteristic Data
Description Videos of people in fabric masks training algorithms to detect biometric hacking attempts.
Data types Video
Tasks Face recognition, Computer Vision
Total number of videos 3,600
Total number of people 30
Labeling Only technical characteristics and metadata (age, gender, ethnicity, glasses, wig, camera, light condition, background)
Gender Male(50%), Female(50%)
Ethnicity Caucasian, African, Asian
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Statistics

Distribution by gender

Technical
Characteristics

Characteristic Data
Video Extensions Mp4, MOV
Video Resolutions Min = 1920х1080, Max = 3840х2160
Video Duration 3-5 second
Number of background 5
Devices Samsung, iPhone, Xiaomi, Huawei
Source and collection methodology: Data was collected via crowdsourcing platforms.

Dataset Use Cases

  • Biometric Security and Authentication

    Improving Face Anti-Spoofing Accuracy

    This face mask detection dataset supports the development of robust face anti-spoofing algorithms by providing high-quality videos containing people wearing different types of face masks, including cloth masks, cotton masks, and surgical masks. The dataset consists of varied backgrounds, lighting conditions, and age groups, enabling trained models to reliably detect presentation attacks and improve face recognition performance across real-world scenarios.

  • Healthcare and Public Safety

    Enhancing Mask Detection for Compliance Monitoring

    This fabric mask dataset enables AI systems to detect and monitor mask usage in public and healthcare settings. The dataset’s videos include medical masks, cloth masks, and other mask types, recorded under multiple lighting conditions and backgrounds. Such datasets containing real-world mask-wearing behavior allow learning algorithms to achieve higher recognition accuracy, supporting safe operations in hospitals, public transport, and crowded environments.

  • AI Research and Machine Learning

    Benchmarking Recognition Models Under Masked Conditions

    Researchers can leverage this face anti-spoofing dataset to study the impact of mask types and occlusions on face recognition systems. The dataset contains annotated metadata for gender, ethnicity, and lighting, allowing trained models to evaluate results obtained across different age groups. This enhances learning algorithms and provides a solid foundation for academic research on presentation attack resilience.

  • Consumer Electronics and Smart Devices

    Optimizing Biometric Systems for Masked Users

    This face anti-spoofing dataset is critical for testing smartphone and camera-based recognition systems. It includes recordings from devices like Samsung, Huawei, Xiaomi, and iPhone, covering multiple mask types and backgrounds. Developers can use this dataset to train models that maintain face recognition accuracy under mask usage, ensuring reliable biometric authentication in everyday applications.

FAQs

What is included in this dataset?
The dataset comprises 3,600 high-resolution video clips of 30 individuals wearing various fabric masks and disguises. Videos include metadata such as age, gender, ethnicity, glasses, wigs, camera type, lighting, and background, with video durations of 3–5 seconds.
What types of annotations are provided?
Each video is labeled with technical metadata, including subject age, gender, ethnicity, mask type, accessories, lighting conditions, camera device, and background. These annotations enable supervised learning for face anti-spoofing and mask detection systems.
Can I request a sample of the dataset before purchasing or downloading it?
Yes. You can request a sample of the dataset to verify video quality, mask diversity, and metadata completeness. Sampling ensures that the dataset meets your requirements for training anti-spoofing models or conducting face mask detection research.
What are the sources of data for Unidata datasets?
Unidata datasets are collected from verified partners and legally compliant sources. The Fabric Masks Dataset was collected via crowdsourcing platforms, capturing videos of people using different fabric masks, wigs, and glasses.
How was the dataset collected?
Data was collected via crowdsourcing platforms, recording individuals in controlled indoor and outdoor environments using devices like Samsung, iPhone, Xiaomi, and Huawei smartphones. The dataset covers five different backgrounds and various lighting conditions to simulate realistic presentation attacks.
Do Unidata datasets follow GDPR or other data privacy regulations?
Yes. All Unidata datasets, including this fabric masks and disguise dataset, comply with GDPR and applicable data protection laws. Data is collected from legally permissible sources, ensuring ethical and lawful usage.
How are Unidata datasets stored?
Unidata stores all datasets on AWS cloud infrastructure, ensuring high availability, scalability, and secure data management. Storage practices follow ISO 27001 and ISO 27701 standards, providing a privacy-focused environment for sensitive biometric data.
How long does it take to receive the dataset?
Once your request is submitted, Unidata reviews the details and completes the necessary documentation. After signing and payment, the dataset is delivered within 3–10 business days.
Still have questions about using Unidata datasets? Read our user-guides

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