---
license: cc-by-nc-nd-4.0
tags:
- Face Mask Detection
- mask detection
- face recognition
- presentation attack detection
- fabric mask
- facial recognition
size_categories:
- 1K<n<10K
task_categories:
- video-classification
---
# Face Mask Detection Dataset
The dataset consists of **3,600** videos featuring **30** people wearing various fabric face masks, captured under diverse conditions. It is designed for research in **presentation attack detection (PAD)**, focusing on challenging **facial recognition** systems and enhancing **fraud prevention** mechanisms.

Researchers can utilize this data for developing advanced **mask detection** and **face recognition** algorithms.  - **[Get the data](https://unidata.pro/datasets/fabric-masks-and-disguise-presentation-attack-dataset/?utm_source=huggingface&utm_medium=referral&utm_campaign=fabric-masks-dataset)**

The videos showcase a wide variety of mask types and designs, worn by individuals from different age groups and ethnicities. The study has placed strong emphasis on variability, including factors like worn masks, the use of glasses or wigs, and changing light conditions and backgrounds.

## 💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at [https://unidata.pro](https://unidata.pro/datasets/fabric-masks-and-disguise-presentation-attack-dataset/?utm_source=huggingface&utm_medium=referral&utm_campaign=fabric-masks-dataset) to discuss your requirements and pricing options.

## Metadata for the dataset
![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F27063537%2Faf00dec6be2e93cbf40f795c6c141a3c%2Fchart%20(1).png?generation=1758142150608875&alt=media)

**Variables in .csv files:**

- person_id: The unique identifier for the genuine participant.
- sample_id: The unique identifier for each video sample.
- gender: Gender of the participant (Male/Female).
- age: The approximate age of the participant.
- class: The type of sample (bona_fide for real, attack for presentation attack).
- impostor_id: For attack samples, the ID of the impostor wearing the mask (e.g., IMP_A); is none for bona fide samples.
- mask_id: For attack samples, the ID of the specific fabric mask used (e.g., MASK_1_A); is none for bona fide samples.
- glasses: Indicates if glasses are present (e.g., rimless, none).
- wig: Indicates if a wig is present (e.g., short_blond, short_dark, none).
- camera: The recording device used (e.g., Iphone, Samsung).
- light_condition: The lighting environment (e.g., natural, artificial, natural_and_artificial, dim_light).
- background: The backdrop of the recording (e.g., white_wall, office_bookshelves, office_brick_wall, window).

This detailed metadata provides a robust foundation for achieving higher detection accuracy, advancing liveness detection methods.

## 🌐 [UniData](https://unidata.pro/datasets/fabric-masks-and-disguise-presentation-attack-dataset/?utm_source=huggingface&utm_medium=referral&utm_campaign=fabric-masks-dataset) provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects