What is Axon Labs?
Axon Labs Datasets offers a catalog of biometric datasets for liveness detection, face recognition, and audio biometrics.
Datasets cover iBeta Level 1–3 attack vectors, including replay, photo print, cutout, paper masks, silicone/latex/3D resin masks, wrapped attacks and other face anti-spoofing scenarios.
Collections include selfies, behavioral videos, NIST-compliant face recognition sets, synthetic children faces, Web IR+RGB captures, and multi-language call center speech corpora for voice biometrics and speech recognition.
Data is delivered in ML-ready formats (MP4/MOV, JPEG/PNG) with CSV metadata and annotations compatible with PyTorch and TensorFlow pipelines.
Balanced train/test splits, demographic diversity, and attack coverage support model training, bias analysis, and pre-certification validation for iBeta and NIST testing.
Custom data collection and supplemental filming address missing attack types or project-specific requirements.
Axon Labs user reviews
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Axon Labs's key features
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Specialized biometric datasets for liveness detection, face recognition, and audio biometrics
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Comprehensive attack-vector coverage across iBeta L1/L2/L3 (print, replay, cutout, paper masks, 3D/silicone/latex/wrapped masks, etc.)
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ML-ready data formats and metadata (MP4/MOV, JPEG/PNG, CSV annotations) compatible with PyTorch and TensorFlow
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ML-focused dataset organization with proper train/test splits, balanced classes, and detailed annotations aligned to certification tests
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Custom data collection and augmentation services to capture missing attack types and produce tailored datasets
Axon Labs use cases
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Train and benchmark robust liveness-detection and face anti-spoofing models using Axon Labs Datasets' ML-ready, annotated samples that cover iBeta Level 1–3 attack types with balanced demographic splits for fair, production-ready performance evaluation
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Develop and validate multi-language voice biometric authentication systems by leveraging the curated speech corpus with precise annotations and custom collection options to optimize speaker recognition and reduce false accepts across languages
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Perform compliance testing, adversarial attack simulations and model hardening using the spoof attack datasets and custom collection features to replicate real-world attacks, measure system resilience, and produce reproducible benchmarks for audits and product demos
Who is it for?
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Machine learning engineers
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Data scientists
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Product managers
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Biometric researchers
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Biometric hobbyists
