iBeta (Level 2) Certified, Single-Image Based Face Liveness Detection (Face Anti Spoofing) Server SDK
-
Updated
Apr 9, 2025 - Python
iBeta (Level 2) Certified, Single-Image Based Face Liveness Detection (Face Anti Spoofing) Server SDK
Silicone mask attack dataset for face anti-spoofing and liveness detection. 12,500+ videos, 18 silicone masks, 40+ accessory combinations. iBeta Level 2 compliant
Display replay attack dataset for face anti-spoofing and liveness detection. 9,000+ videos from 6,500+ participants across PC monitors and mobile devices
Partial paper mask attack dataset for face anti-spoofing, liveness detection, and presentation attack detection (PAD). 3,000 videos, 50 participants, dual-device capture.
Liveness detection dataset for face anti-spoofing model training. Sample preview of Axon Labs' commercial PAD library covering iBeta Level 1, 2, and 3 attacks
Face anti-spoofing dataset for AI model training. Sample preview of Axon Labs' commercial PAD library covering paper, replay, and 3D mask attacks across iBeta certification levels
Cardboard mask attack dataset with real accessories (wigs, glasses, hats) for face anti-spoofing, liveness detection, and PAD. 3,000 videos, 50 participants, multi-device capture
iBeta Level 1 dataset for face anti-spoofing and liveness detection. 30,000+ PAD attack videos (paper, cutout, replay) from 85+ participants. ISO/IEC 30107-3 compliant
iBeta Level 2 dataset for face anti-spoofing and liveness detection. 25,000+ videos from 150+ IDs with silicone, latex, wrapped 3D, and cloth mask attacks
Train presentation attack detection and liveness models with this dataset for face anti-spoofing.
Add a description, image, and links to the ibeta topic page so that developers can more easily learn about it.
To associate your repository with the ibeta topic, visit your repo's landing page and select "manage topics."