UniDataPro/fabric-masks-dataset

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

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.

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Metadata for the dataset

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.

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