Error code:   ConfigNamesError
Exception:    DataFilesNotFoundError
Message:      No (supported) data files found in AxonData/2D_print_attack_dataset
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 73, 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 347, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1904, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1885, in dataset_module_factory
                  return HubDatasetModuleFactoryWithoutScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1270, in get_module
                  module_name, default_builder_kwargs = infer_module_for_data_files(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 597, in infer_module_for_data_files
                  raise DataFilesNotFoundError("No (supported) data files found" + (f" in {path}" if path else ""))
              datasets.exceptions.DataFilesNotFoundError: No (supported) data files found in AxonData/2D_print_attack_dataset

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Photo Print Attacks Dataset: 1K Individuals

Face Anti-Spoofing Liveness dataset videos with Zoom in effect, High-Res Print

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Photo Print attack dataset (1K individuals+) for Presentation Attack Detection level 1 (PAD)

This dataset focuses on photo print attacks which is used by both iBeta and NIST FATE to assess liveness detection algorithms. This dataset is tailored for training AI models to identify photo print attacks on individuals. Print photo attacks include Zoom effects as mandated by NIST FATE for improved AI training.

Dataset Description:

1,000+ Participants: Engaged in the project
Diverse Representation: Balanced mix of genders and ethnicities
1,000+ Photo Print Attacks: Executed on the participants

Photo Print attack description:

Each attack comprises of 15-20 sec. video with Zoom in effects
High-quality photos with realistic colors
No visible image borders during the Zoom-in phase
Paper attacks conducted on flat photos with a straight view on the camera (not bent or skewed)

Potential Use Cases:

Liveness detection: This dataset is ideal for training and evaluating liveness detection models, enabling researchers to distinguish between selfies and photo print attacks with high accuracy

Keywords: Print photo attack dataset, Antispoofing for AI, Liveness Detection dataset for AI, Spoof Detection dataset, Facial Recognition dataset, Biometric Authentication dataset, AI Dataset, PAD Attack Dataset, Anti-Spoofing Technology, Facial Biometrics, Machine Learning Dataset, Deep Learning

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