Error code:   ConfigNamesError
Exception:    RuntimeError
Message:      Dataset scripts are no longer supported, but found low_quality_webcam_video_attacks.py
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1031, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 989, in dataset_module_factory
                  raise RuntimeError(f"Dataset scripts are no longer supported, but found {filename}")
              RuntimeError: Dataset scripts are no longer supported, but found low_quality_webcam_video_attacks.py

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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.

The dataset is created on the basis of iBeta Level 1 Dataset

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.

👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset of 25,000+ human images & videos - Full dataset

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