|
|
--- |
|
|
license: cc-by-4.0 |
|
|
task_categories: |
|
|
- image-feature-extraction |
|
|
- image-classification |
|
|
- video-classification |
|
|
language: |
|
|
- en |
|
|
tags: |
|
|
- liveness detection |
|
|
- anti-spoofing |
|
|
- biometrics |
|
|
- facial recognition |
|
|
- machine learning |
|
|
- deep learning |
|
|
- AI |
|
|
- paper mask attack |
|
|
- iBeta certification |
|
|
- PAD attack |
|
|
- security |
|
|
- ibeta |
|
|
- face recognition |
|
|
- pad |
|
|
- authentication |
|
|
- fraud |
|
|
size_categories: |
|
|
- 1K<n<10K |
|
|
--- |
|
|
|
|
|
## Face Anti Spoofing Replay Dataset |
|
|
|
|
|
Liveness Detection: Replay attacks. 5,000+ videos of display replay monitor attacks 12+ sec and real photos. The attacks provide diversity of lighting, devices, and screens |
|
|
|
|
|
## 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 💰 |
|
|
|
|
|
 |
|
|
Left: Real selfie; Right: Display attack |
|
|
 |
|
|
Left: Real selfie; Right: Display attack |
|
|
|
|
|
## Dataset Description: |
|
|
- Over 1,000 individuals shared selfies |
|
|
- Balanced mix of genders and ethnicities |
|
|
- More than 5,000 display attacks crafted from these selfies |
|
|
|
|
|
## Real Life Selfies Description: |
|
|
|
|
|
- Each person provided one selfie |
|
|
- Selfies are at least 720p quality |
|
|
- Faces are clear with no filters |
|
|
|
|
|
## Replay display attacks description: |
|
|
|
|
|
- Videos last at least 12 seconds |
|
|
- Cameras move slowly, showing attacks from various angles |
|
|
|
|
|
## Potential Use Cases: |
|
|
- Liveness detection: This dataset is ideal for training and evaluating liveness detection models, enabling researchers to distinguish between selfies and replay display attacks with high accuracy |
|
|
|
|
|
- Keywords: Display attacks, Antispoofing, Liveness Detection, Spoof Detection, Facial Recognition, Biometric Authentication, Security Systems, AI Dataset, Replay Attack Dataset, Anti-Spoofing Technology, Facial Biometrics, Machine Learning Dataset, Deep Learning |
