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--- |
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license: cc-by-4.0 |
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task_categories: |
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- image-feature-extraction |
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- image-classification |
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- video-classification |
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language: |
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- en |
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tags: |
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- liveness detection |
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- anti-spoofing |
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- biometrics |
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- facial recognition |
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- machine learning |
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- deep learning |
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- AI |
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- paper mask attack |
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- iBeta certification |
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- PAD attack |
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- security |
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- ibeta |
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- face recognition |
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- pad |
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- authentication |
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- fraud |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Wrapped 3D Attacks Dataset |
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## Full version of dataset is availible for commercial usage - leave a request on our website [Axon Labs](https://axonlabs.pro/?utm_source=hugging-face&utm_medium=cpc&utm_campaign=profile&utm_content=profile_link) to purchase the dataset 💰 |
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## Introduction |
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This dataset is designed to enhance Liveness Detection models by simulating Wrapped 3D Attacks — a more advanced version of 3D Print Attacks, where facial prints include 3D elements and additional attributes. It is particularly useful for iBeta Level 2 certification and anti-spoofing model training |
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## Dataset summary |
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- Dataset Size: ~2k videos shoot on 20 IDs demonstrating various spoofing attacks |
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- Active Liveness Features: Includes zoom-in and zoom-out to enhance training scenarios |
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- Attributes: Different hairstyles, glasses, wigs and beards to enhance diversity |
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- Variability: 3 indoor locations with different types of lighting: from warm to cold |
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- Main Applications: Preparation for iBeta Level 2 certification, Active and passive liveness for anti spoofing systems |
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## Source and collection methodology |
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The videos capture realistic spoofing conditions using different recording devices and variety of environments. Additionally, each attack video employs a zoom-in effect, adding to its effectiveness in active liveness detection. The videos were shot using a back-facing camera |
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## To create wrapped 3D attacks, we: |
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- Constructed 3D facial structures by cutting out A4-sized face prints, shaping volume for the nose, forehead, and chin, and mounting them on mannequin heads or cylindrical objects |
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- Added attributes, including wigs, beards, mustaches, glasses, hats, and hoods, to increase spoofing complexity |
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- Simulated a human torso using clothing on mannequins, chairs, or surface |
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## Best Uses |
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- **iBeta Level 2 Certification Compliance:** |
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Helps to train the models for iBeta level 2 certification tests |
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Allows pre-certification testing to assess system performance before submission |
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- **Inhouse Liveness Detection Models:** |
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Used for training and validation of anti-spoofing models |
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Enables testing of existing algorithms and identification of their vulnerabilities against spoofing attacks |
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## Conclusion |
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With its comprehensive features and simulation of real attack scenarios, the Wrapped 3D Attack Dataset is indispensable for anyone involved in developing and certifying facial recognition and liveness detection technologies. Utilize this dataset to strengthen your systems against the most deceptive digital security threats. |
