UniDataPro/printed-2d-masks-attacks

2D Masks Attack for facial recogniton system

The dataset consists of 4,800+ videos of people wearing of holding 2D printed masks filmed using 5 devices. It is designed for liveness detection algorithms, specifically aimed at enhancing anti-spoofing capabilities in biometric security systems.

By leveraging this dataset, researchers can create more sophisticated recognition system, crucial for achieving iBeta Level 1 & 2 certification – a key standard for secure and reliable biometric systems designed to combat spoofing and fraud. - Get the data

Attacks in the dataset

The attacks were recorded in diverse settings, showcasing individuals with various attributes. Each video includes human faces adorned with 2D printed masks to mimic potential spoofing attempts in facial recognition systems.

Variants of backgrounds and attributes in the dataset:

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Metadata for the dataset

Variables in .csv files:

• name: filename of the printed 2D mask
• path: link-path for the original video
• type: type(wearing or holding) of printed mask

Researchers are developing advanced anti-spoofing detection techniques to enhance security systems against attacks using face masks.This focus on face masks allows researchers to train and test detection algorithms specifically designed to differentiate between genuine human faces and these increasingly sophisticated mask-based spoofing attempts.

Frequently Asked Questions

What video quality is available in the printed 2D masks attacks dataset?

The dataset contains high-resolution video suitable for detailed facial and presentation-attack analysis. The recordings range from 1920 × 1080 Full HD to 3840 × 2160 4K, allowing you to preserve facial details during frame extraction and preprocessing.

Who can benefit from the printed 2D masks attacks dataset?

The printed 2D masks dataset can benefit biometric security teams, facial recognition engineers, computer vision researchers, liveness detection developers, fintech companies, identity verification providers, cybersecurity teams, and academic institutions. It is particularly relevant to projects involving presentation attack detection, biometric authentication, fraud prevention, and iBeta-oriented testing.

How much background variation is included in the 2D mask attack videos?

The masks biometric attacks dataset was recorded against nine different backgrounds. This variation allows you to test whether a liveness detection model is learning characteristics of the printed mask itself or inadvertently relying on environmental cues.

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