---
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