README.md

metadata

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 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