UniDataPro/3d-mask-attack

3D Mask Attack for detection methods

Dataset comprises 3,500+ videos captured using 5 different devices, featuring individuals holding photo fixed on cylinder designed to simulate potential presentation attacks against facial recognition systems. It supports research in attack detection and improves spoofing detection techniques, specifically for fraud prevention and compliance with iBeta Level 1 certification standards.

By utilizing this dataset, researchers can enhance liveness detection and improve face presentation techniques, ultimately contributing to more robust biometric security measures. - Get the data

Attacks in the dataset

💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at https://unidata.pro to discuss your requirements and pricing options.

Researchers can utilize this dataset to explore detection technology and recognition algorithms that aim to prevent impostor attacks and improve authentication processes.

Metadata for the dataset

This dataset serves as a crucial tool for enhancing liveness detection technologies and developing effective anti-spoofing algorithms, which are essential for maintaining the integrity of biometric verification processes.

Frequently Asked Questions

What video formats and resolutions are available?

The dataset provides videos in MP4 and MOV formats with resolutions ranging from 1920 × 1080 to 3840 × 2160. High-resolution recordings support the development of facial presentation attack detection models that require fine-grained visual information.

What is the video duration and why does it matter for liveness detection?

Each recording is approximately four seconds long, providing a short temporal sequence for presentation-attack analysis. This duration can be useful for models that analyze multiple frames rather than making a decision from a single image.

🌐 UniData provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects