Update README.md

README.md

@@ -20,51 +20,93 @@ pretty_name: Liveness Detection Dataset
 size_categories:
 - 100K<n<1M
 ---
-100,000+ spoofing videos for liveness detection. A general-purpose liveness detection dataset designed for training and evaluating face anti-spoofing models. Unlike narrow public datasets that cover only one or two attack types, this dataset combines all major presentation attack categories in a single resource — paper attacks, replay attacks, 3D masks (silicone, latex, paper-wrapped, resin), and more
+# Face Liveness Detection Dataset for Anti-Spoofing & PAD Certification
+100,000+ spoofing videos for liveness detection
+
+A comprehensive face liveness detection dataset for face anti-spoofing, biometric face recognition, and presentation attack detection (PAD) systems. Unlike narrow public benchmarks that cover only one or two attack types, this dataset combines all major presentation attack categories in a single resource: paper attacks, replay attacks, and 3D mask attacks (silicone, latex, paper-wrapped, resin, cloth), delivering 100,000+ videos across 11+ labeled attack types mapped to iBeta Level 1, Level 2, and
+Level 3 PAD certification 
+
+## What Is Face Liveness Detection?
+
+Face liveness detection is the biometric verification step that determines whether a captured face belongs to a live person rather than a spoofed presentation (printed photo, video replay, 3D mask). It is the core defense against presentation attacks in face recognition systems used for eKYC, fintech onboarding, banking authentication, and government identity verification
+
+Robust liveness detection requires training on diverse attack vectors, which is why this aggregated dataset combines multiple attack categories rather than focusing on a single one
+
+## Why Use This Dataset
+
+- **All major attack vectors in one resource** - reduces dataset assembly overhead vs combining CASIA-FASD + Replay-Attack + OULU-NPU + others
+- **Direct iBeta mapping** - each attack labeled with its iBeta level (L1/L2/L3) for certification-ready training
+- **Modern capture quality** - current-generation smartphones (iPhone 14/13 Pro, Galaxy S23, Pixel 7, Redmi, Honor 70), not academic 2015-era setups
+- **Production-aligned conditions** - indoor and outdoor environments, varied lighting, balanced demographics
+
+## Quick Stats
+- **~100,000 videos**
+- **11+ attack types** (expandable on request)
+- **Capture devices:** iPhone 14, iPhone 13 Pro, Samsung Galaxy S23, Google Pixel 7, Xiaomi Redmi, Honor 70, and others
+- **Indoor and outdoor environments**
+- **Balanced gender mix and multi-ethnic representation** (Caucasian, Black, Asian, Latinx)
+- **Active liveness phases:** fixed, zoom-in, zoom-out
+
+## Full version of the dataset is available for commercial usage. Leave a request on our website [Axonlabs](https://axonlab.ai/?utm_source=hugging-face&utm_medium=cpc&utm_campaign=profile&utm_content=profile_link) to purchase the dataset 💰
+## For feedback and additional sample requests, please contact us!
 
 ## Attack Types in This Dataset
-Each video/image sequence is labeled with one of the following classes:
-| Label | Type | Category |
-|---|---|---|
-| `live` | Bona fide | Genuine |
+
+Each video sequence is labeled with one of the following classes:
+
+| Label | Type | iBeta Level |
+|-------|------|-------------|
+| `live` | Bona fide (genuine face) | — |
 | `photo_print` | Printed photo | iBeta L1 (2D) |
-| `3d_paper_mask` | 3D paper mask | iBeta L1/L2 |
-| `wrapped_3d_print` | Paper-wrapped 3D | iBeta L2 |
-| `cylinder_3d_mask` | Cylinder paper mask | iBeta L1 |
 | `cutout_2d_mask` | Cut-out 2D mask | iBeta L1 |
 | `on_actor_print` | Worn paper attack | iBeta L1 |
+| `cylinder_3d_mask` | Cylinder paper mask | iBeta L1 |
 | `mobile_replay` | Phone screen replay | iBeta L1 |
 | `display_replay` | Monitor/tablet replay | iBeta L1 |
+| `3d_paper_mask` | 3D paper mask | iBeta L1 / L2 |
+| `wrapped_3d_print` | Paper-wrapped 3D | iBeta L2 |
 | `silicone_mask` | Silicone 3D mask | iBeta L2 |
 | `latex_mask` | Latex mask | iBeta L2 |
 | `cloth_3d_mask` | Fabric 3D mask | iBeta L2 |
-| `resin_mask` | High-fidelity resin | iBeta L3 |
+| `resin_mask` | High-fidelity resin mask | iBeta L3 |
 
-## Full version of the dataset is available for commercial usage. Leave a request on our website [Axonlabs](https://axonlab.ai/?utm_source=hugging-face&utm_medium=cpc&utm_campaign=profile&utm_content=profile_link) to purchase the dataset 💰
-## For feedback and additional sample requests, please contact us!
+## Example Use Cases
 
-## Quick Stats
-- ~100,000 videos
-- 11+ attack types covered and can be collected more
-- Actual capture devices (iPhone 14/13 Pro, Galaxy S23, Pixel 7, Redmi, Honor 70, etc.)
-- Indoor + outdoor environments
-- Balanced gender and ethnicity
+- **Train binary anti-spoofing classifier** - bona fide vs all attack types combined
+- **Multi-class attack-type classifier** - useful for explainable AI ("this is a silicone mask attack" vs "this is a replay") and model debugging (which attack types fail?)
+- **iBeta certification preparation** - filter dataset to L1 attacks → train → benchmark; same for L2 and L3
+- **Cross-attack generalization research** - analyze performance gaps between paper, replay, and 3D mask attacks
 
-## Example Use Cases
-1. Train binary anti-spoofing classifier
-- Bona fide vs all attack types combined
-2. Multi-class attack-type classifier
-- Useful for explainable AI: "this is a silicone mask attack" vs "this is a replay"
-- Useful for model debugging (which attack types fail?)
-3. iBeta certification prep
-- Filter dataset to L1 attacks → train → benchmark
-- Same for L2, L3
-- Verify model APCER/BPCER thresholds
+## Academic Reference
 
-## Why Use This Dataset
-- Reduces dataset assembly overhead - one resource instead of combining CASIA-FASD + Replay-Attack + OULU-NPU + others
-- Maps to real certification protocols - iBeta Level 1/2/3 categories
-- Modern capture quality - recent smartphones, not academic 2015-era setups
+This commercial dataset complements canonical academic benchmarks in face anti-spoofing research:
+
+- **Idiap Replay-Attack** — replay attack baseline
+- **Idiap CSMAD / 3DMAD** — silicone and 3D mask baselines
+- **OULU-NPU** — mobile face liveness benchmark
+- **MSU-MFSD** — mobile spoofing detection benchmark
+- **CASIA-FASD / CASIA-SURF** — 2D and multi-modal benchmarks
+
+This dataset extends those research lines with significantly more participants, modern smartphone capture conditions, broader demographic diversity, and direct iBeta certification mapping, designed for production face recognition systems rather than research benchmarks alone
+
+## Related Datasets by Axon Labs
+
+- [Silicone Mask Dataset](https://huggingface.co/datasets/AxonData/silicone-mask-dataset) - high-realism silicone masks for iBeta Level 2
+- [3D Paper Mask Attack Dataset](https://huggingface.co/datasets/AxonData/3D_paper_mask_attack_dataset_for_Liveness) - volumetric paper attacks
+- [Advanced Paper Attacks Dataset](https://huggingface.co/datasets/AxonData/face-anti-spoofing-advanced-paper-attacks) - 7 advanced paper attack scenarios
+- [Photo Print Attack Dataset](https://huggingface.co/datasets/AxonData/Anti_spoofing_dataset_Print_attack) - basic 2D photo print attacks
+- [Display Replay Attacks Dataset](https://huggingface.co/datasets/AxonData/Display_replay_attacks) - display-based replay attacks
+- [Replay Attack Dataset](https://huggingface.co/datasets/AxonData/replay-attack-dataset)
+  - broader replay coverage
+- [iBeta Level 1 Certification Dataset](https://huggingface.co/datasets/AxonData/ibeta-level-1-certification)
+- [iBeta Level 2 Certification Dataset](https://huggingface.co/datasets/AxonData/iBeta-Level-2-Certification-Dataset)
+- [iBeta Level 3 Dataset](https://huggingface.co/datasets/AxonData/ibeta-level-3-dataset)
 
 ## About Axon Labs
-[Axon Labs](https://axonlab.ai/?utm_source=hugging-face&utm_medium=cpc&utm_campaign=profile&utm_content=profile_link) builds biometric AI training datasets. Trusted by 21% of iBeta-certified biometric companies. Specializing in liveness detection, face recognition, and voice anti-spoofing data
+
+Axon Labs builds biometric AI training datasets. We specialize in face liveness detection, face recognition, and voice anti-spoofing data for production identity verification, eKYC, fintech, and government applications
+
+## Commercial Access
+
+Sample subset publicly available for evaluation. For full commercial dataset access, pricing, and licensing terms, contact [sales@axonlabs.pro](mailto:sales@axonlabs.pro) or visit
+[axonlab.ai](https://axonlab.ai/)