Update README.md

README.md

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-license: cc-by-nc-4.0
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+---
+license: cc-by-nc-4.0
+task_categories:
+- image-feature-extraction
+- image-classification
+- video-classification
+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
+
+## 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