Datasets:
metadata
license: cc-by-nc-4.0
task_categories:
- image-classification
- image-feature-extraction
tags:
- palm
- palmprint
- hand-palm
- hand-detection
- palm-detection
- palm-recognition
- biometric-authentication
- identity-verification
- computer-vision
- smartphone
- mobile-biometrics
- cross-device
- demographic-diversity
size_categories:
- 10K<n<100K
language:
- en
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
Comparison With Public Palmprint Databases
| Dataset | Subjects | Images | Capture Method |
|---|---|---|---|
| This dataset | 2,000 | 24,000 | 200+ smartphones |
| Tongji Contactless | 300 | 12,000 | Custom device |
| PolyU v3.0 | 600 | 12,000 | Custom device |
| IITD Touchless | 230 | 2,601 | Fixed setup |
| 11K Hands | 190 | 11,076 | Single camera |
| MPD (DeepMPV) | 200 | 16,000 | Smartphones |
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