Error code:   ConfigNamesError
Exception:    RuntimeError
Message:      Dataset scripts are no longer supported, but found silicone-masks-biometric-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 silicone-masks-biometric-attacks.py

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Silicone Masks Biometric Attack Dataset

The anti spoofing dataset consists of videos of individuals and attacks with printed 2D masks and silicone masks . Videos are filmed in different lightning conditions (in a dark room, daylight, light room and nightlight). Dataset includes videos of people with different attributes (glasses, mask, hat, hood, wigs and mustaches for men).

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.

👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset - Full dataset

Types of videos in the dataset:

real - real video of the person
outline -video of the person wearing a printed 2D mask
silicone - video of the person wearing a silicone mask

Types and number of videos in the full dataset:

2885 real videos of people
2859 videos of people wearing silicone mask
48 videos of people wearing a 2D mask.

Gender of people in the dataset:

women: 2685
men: 3107

The dataset serves as a valuable resource for computer vision, anti-spoofing tasks, video analysis, and security systems. It allows for the development of algorithms and models that can effectively detect attacks.

Studying the dataset may lead to the development of improved security systems, surveillance technologies, and solutions to mitigate the risks associated with masked individuals carrying out attacks.

🧩 This is just an example of the data. Leave a request here to learn more

Content

real - contains of real videos of people,
mask - contains of videos with people wearing a printed 2D mask,
silicone - contains of videos with people wearing a silicone mask,
dataset_info.csvl - includes the information about videos in the dataset

File with the extension .csv

video: link to the video,
type: type of the video

Attacks might be collected in accordance with your requirements.

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