iBeta Level 2 Liveness Detection, Anti spoofing dataset

The biometric attack dataset consists of six diffrent types of videos: real face videos, 2D mask attacks with and without eyes cutting, 3D cardboard attacks, attacks with latex and silicon masks.

The dataset comprises videos of genuine facial presentations using various methods, including 3D masks and photos, as well as real and spoof faces. It proposes a novel approach that learns and extracts facial features to prevent spoofing attacks, based on deep neural networks and advanced biometric techniques.

Our results show that this technology works effectively in securing most applications and prevents unauthorized access by distinguishing between genuine and spoofed inputs. Additionally, it addresses the challenging task of identifying unseen spoofing cues, making it one of the most effective techniques in the field of anti-spoofing research.

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All the attacks enabled the liveness detection system to pass the iBeta testing at level 2.

iBeta Level 1 Testing

The iBeta Level 1 Liveness Detection Dataset: Link to the Kaggle

Attributes

The videos were filmed on various backgrounds and using additional accessories such as faked facial hair, scarves, hats, and others.

Details

There are 21 unique persons in the dataset. Each video was filmed on an Apple iPhone and Google Pixel with high-quality resolution (> 1920 x 1080).

💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/datasets to discuss your requirements, learn about the price and buy the dataset

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