Low Quality Live Attacks - Biometric Attack dataset

The anti spoofing dataset includes live-recorded Anti-Spoofing videos from around the world, captured via low-quality webcams with resolutions like QVGA, QQVGA and QCIF. The videos were gathered by capturing faces of genuine individuals presenting spoofs, using facial presentations. Our dataset proposes a novel approach that learns and detects spoofing techniques, extracting features from the genuine facial images to prevent the capturing of such information by fake users.

The dataset contains images and videos of real humans with various views, and colors, making it a comprehensive resource for researchers working on anti-spoofing technologies.

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The dataset provides data to combine and apply different techniques, approaches, and models to address the challenging task of distinguishing between genuine and spoofed inputs, providing effective anti-spoofing solutions in active authentication systems. These solutions are crucial as newer devices, such as phones, have become vulnerable to spoofing attacks due to the availability of technologies that can create replays, reflections, and depths, making them susceptible to spoofing and generalization.

Our dataset also explores the use of neural architectures, such as deep neural networks, to facilitate the identification of distinguishing patterns and textures in different regions of the face, increasing the accuracy and generalizability of the anti-spoofing models.

Webcam Resolution

The collection of different video resolutions is provided, like:

QVGA (320p x 240p),
QQVGA (120p x 160p),
QCIF (176p x 144p) and others.

Metadata

Each attack instance is accompanied by the following details:

Unique attack identifier
Identifier of the user recording the attack
User's age
User's gender
User's country of origin
Attack resolution

Additionally, the model of the webcam is also specified.

Metadata is represented in the file_info.csv.

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