The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: KeyError
Message: 'selfie'
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 322, in compute
compute_first_rows_from_parquet_response(
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 88, in compute_first_rows_from_parquet_response
rows_index = indexer.get_rows_index(
File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 640, in get_rows_index
return RowsIndex(
File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 521, in __init__
self.parquet_index = self._init_parquet_index(
File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 538, in _init_parquet_index
response = get_previous_step_or_raise(
File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 591, in get_previous_step_or_raise
raise CachedArtifactError(
libcommon.simple_cache.CachedArtifactError: The previous step failed.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 96, in get_rows_or_raise
return get_rows(
File "/src/libs/libcommon/src/libcommon/utils.py", line 197, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 73, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1389, in __iter__
for key, example in ex_iterable:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 234, in __iter__
yield from self.generate_examples_fn(**self.kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 126, in _generate_examples
example[field_name] = {"path": example["__key__"] + "." + field_name, "bytes": example[field_name]}
KeyError: 'selfie'Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Anti Spoofing Real - Liveness Detection dataset
The Biometric Attack dataset consists of 98,000 videos and selfies from people from 170 countries. 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 resolutions, views, and colors, making it a comprehensive resource for researchers working on anti-spoofing technologies.
The dataset includes 2 different types of files:
- Photo - a selfie of a person from a mobile phone, the person is depicted alone on it, the face is clearly visible.
- Video - filmed on the front camera, on which a person moves his/her head left, right, up and down. Duration of the video is from 10 to 20 seconds.
Image
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.
💴 For Commercial Usage: Full version of the dataset includes 50 000+ sets of files, leave a request on TrainingData to buy the dataset
Metadata for the full dataset:
- assignment_id - unique identifier of the media file
- worker_id - unique identifier of the person
- age - age of the person
- true_gender - gender of the person
- country - country of the person
- ethnicity - ethnicity of the person
- video_extension - video extensions in the dataset
- video_resolution - video resolution in the dataset
- video_duration - video duration in the dataset
- video_fps - frames per second for video in the dataset
- photo_extension - photo extensions in the dataset
- photo_resolution - photo resolution in the dataset
💴 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
Content
The folder "samples" includes 30 folders:
- corresponding to each person in the sample
- containing of selfie and video of the individual
File with the extension .csv
includes the following information for each media file:
- phone: the device used to capture the media files,
- selfie_link: the URL to access the photo
- video_link: the URL to access the video
- worker_id: the identifier of the person who provided the media file,
- age: the age of the person,
- country: the country of origin of the person,
- gender: the gender of the person,
- selfie_file_type: the type of the photo,
- video_file_type: the type of the video
TrainingData provides high-quality data annotation tailored to your needs.
More datasets in TrainingData's Kaggle account: https://www.kaggle.com/trainingdatapro/datasets
TrainingData's GitHub: https://github.com/Trainingdata-datamarket/TrainingData_All_datasets
keywords: ibeta level 1, ibeta level 2, liveness detection systems, liveness detection dataset, biometric dataset, biometric data dataset, biometric system attacks, anti-spoofing dataset, face liveness detection, deep learning dataset, face spoofing database, face anti-spoofing, face detection, face identification, face recognition, human video dataset, video dataset, phone attack dataset, face anti spoofing, large-scale face anti spoofing, rich annotations anti spoofing dataset
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