UniDataPro/2d-printed-mask-dataset

2D Mask Attack Dataset - 26 436 videos

The dataset comprises 26,436 videos of real faces, 2D print attacks (printed photos), and replay attacks (faces displayed on screens), captured under varied conditions. Designed for attack detection research, it supports the development of robust face antispoofing and spoofing detection methods, critical for facial recognition security.

Ideal for training models and refining anti-spoofing methods, the dataset enhances detection accuracy in biometric systems. - Get the data

Frequently Asked Questions

Who can benefit from this 2D printed masks dataset?

The 2D printed masks dataset is valuable for biometric security vendors, AI researchers, financial institutions, mobile authentication developers, and academic laboratories working on presentation attack detection.

What types of presentation attacks are included in this face anti-spoofing dataset?

This face anti-spoofing dataset includes multiple presentation attack types designed to simulate real-world biometric fraud scenarios. Researchers can work with both 2D print attacks and silicone mask attacks, allowing models to learn how different spoofing techniques affect facial recognition systems. The dataset also includes images and videos featuring additional appearance variations such as wigs, glasses, facial hair, face masks, and different clothing styles.

How was the data collected for this presentation attack dataset?

The dataset was collected by a UniData partner using a structured acquisition process designed specifically for biometric security research.

Example of the data

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Researchers can leverage this training data to improve detection accuracy, validate models trained on adversarial examples, and advance recognition systems against sophisticated masked attacks.

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