| | --- |
| | license: cc-by-nc-4.0 |
| | task_categories: |
| | - image-feature-extraction |
| | - image-classification |
| | - video-classification |
| | language: |
| | - en |
| | tags: |
| | - palm-recognition |
| | - biometrics |
| | - biometric-authentication |
| | - palmprint |
| | - contactless |
| | - smartphone |
| | - hand-recognition |
| | - computer-vision |
| | - dual-camera |
| | - demographic-diversity |
| | - cross-device |
| | - mobile-biometrics |
| | - pattern-recognition |
| | - identity-verification |
| | --- |
| | 24,000 high-quality images from 2,000 diverse participants worldwide - smartphone palm recognition dataset for biometric authentication |
| |
|
| | ## Participants & Demographics |
| | - 2,000 diverse participants from multiple countries |
| | - Balanced gender representation |
| | - 6+ ethnic groups: Black, South Asian, Caucasian, Arab/Middle Eastern, Hispanic, East Asian |
| | - Age range: Under 20 to 50+ years |
| | - Both right-handed and left-handed individuals |
| |
|
| | ## Image Capture |
| | - Smartphone-based: 200+ different models (iOS and Android) |
| | - Dual-camera: Both front-facing and back-facing cameras |
| | - Multiple backgrounds: 3 variations per configuration |
| | - Complete coverage: Both left and right hands |
| | - 12 images per participant |
| |
|
| | ## Rich metadata included |
| | - Format: JSON and CSV |
| | - Demographics: Gender, ethnicity, birth year, profession |
| | - Technical: Device model, camera type, handedness |
| | - File mappings: Links to all 12 images per participant |
| |
|
| | ## Full version of dataset is availible 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 💰 |
| |
|
| | ## Use cases |
| | - **Biometric Authentication:** Train palm recognition systems for secure authentication in mobile apps, banking, and access control |
| | - **Cross-Device Testing:** Test algorithm performance across 200+ different smartphone models and camera qualities |
| | - **Fairness Research:** Evaluate and improve model accuracy across different ethnicities, ages, and genders |
| | - **Multi-Modal Biometrics:** Combine palm recognition with face, fingerprint, or iris for enhanced security |
| |
|
| | ## Why This Dataset? |
| | - 2-3x larger than comparable public datasets |
| | - Real smartphone capture (not specialized scanners) |
| | - Comprehensive demographic diversity |
| | - Dual-camera data for robustness testing |
| | - Rich metadata for fairness research |
| |
|
