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Transfer Learning for eKYC

Definition

Transfer learning uses models pre-trained on large datasets as starting points for eKYC-specific tasks — dramatically reducing the data and compute needed to achieve high accuracy.

Transfer Learning Strategies

graph TD
    A[Pre-trained Model<br/>ImageNet, COCO, MS1MV2] --> B{Strategy}
    B --> C[Feature Extraction<br/>Freeze backbone, train head only]
    B --> D[Fine-tuning<br/>Unfreeze backbone, train all layers]
    B --> E[Progressive unfreezing<br/>Gradually unfreeze from top layers]

    C --> F["Fast, low data needed<br/>Good when target domain is similar"]
    D --> G["Best accuracy<br/>Needs more data, risk of forgetting"]
    E --> H["Balanced approach<br/>Preserves pre-trained features while adapting"]

Common Pre-Training Sources for eKYC

Task Pre-trained On Transfer To
Face liveness ImageNet → face datasets Liveness-specific fine-tuning
Face recognition MS1MV2 / Glint360K Domain-specific face matching
Document OCR Synthetic text → real documents ID-specific text recognition
Document forensics ImageNet → manipulation detection Document-specific tampering
Document classification ImageNet → document datasets ID document classification

Fine-Tuning Best Practices

Practice Details
Lower learning rate 10-100x lower than training from scratch
Freeze early layers Low-level features (edges, textures) transfer well
Larger learning rate for head New classification head needs higher LR
Data augmentation Critical when target dataset is small
Early stopping Monitor validation loss to prevent overfitting

Key Takeaways

Summary

  • Transfer learning is essential for eKYC — reduces data requirements 10-100x
  • ImageNet pre-training is the starting point for most vision tasks
  • Face-specific pre-training (MS1MV2) is critical for recognition and liveness
  • Progressive unfreezing balances accuracy with preventing catastrophic forgetting
  • Without transfer learning, most eKYC tasks would need prohibitively large labeled datasets