UniDataPro/ibeta-level-1-certification

iBeta Level 1 Dataset: Facial Liveness Detection and Anti-Spoofing

The dataset consists of more than 28,800 video attacks of 7 different types specifically curated for a passing iBeta Level 1 and getting a certification. It is compliant with the ISO 30107-3 standard, which sets the highest quality requirements for biometric testing and attack detection.

By geting the iBeta Level 1 certification, biometric technology companies demonstrate their commitment to developing robust and reliable biometric systems that can effectively detect and prevent fraud - Get the data.

Attacks in the dataset

This dataset is designed to evaluate the performance of face recognition and authentication systems in detecting presentation attacks, it includes different pad tests.

Each attack was filmed on an Apple iPhone and Google Pixel.

1. 2D Mask: printed photos of people cut out along the contour
2. Wrapped 2D Mask: printed photos of people attached to a cylinder
3. 2D Mask with Eyeholes: printed photos of people with holes for eyes
4. 3D Mask: portraits consisting of several connected cardboard masks
5. Smartphone Replay: a person's photo demonstrated on a phone screen
6. PC Replay: a person's photo demonstrated on a computer screen
7. Real Person: real videos of people

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Metadata for the dataset

The iBeta Level 1 dataset is an essential tool for the biometrics industry, as it helps to ensure that biometric systems meet the highest standards of anti-spoofing technology. This dataset is used by various biometric companies in various applications and products to test and improve their biometric authentication solutions, face recognition systems, and facial liveness detection methods.

Frequently Asked Questions

Who can benefit from this iBeta Level 1 Certification Dataset?

This iBeta dataset can benefit biometric security researchers, computer vision engineers, liveness detection developers, identity verification providers, fintech companies, and teams developing Presentation Attack Detection systems. It is especially useful for organizations that need to evaluate facial recognition systems against common physical spoofing scenarios before deployment.

What facial characteristics are represented in the dataset?

The iBeta dataset includes metadata describing characteristics such as baldness, beard or moustache, makeup, scars, piercings, and the absence of these features. These attributes introduce natural facial variation that can affect how a biometric model interprets a presentation.

How were the iBeta Level 1 recordings collected?

Unlike datasets assembled from miscellaneous online sources, this collection was captured by the UniData team in a rented studio. A controlled studio environment provides a standardized acquisition process while still allowing variation across participants, devices, backgrounds, and facial characteristics.

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