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Self-Supervised Learning

Definition

Self-supervised learning (SSL) trains models on unlabeled data by creating pretext tasks — the model learns rich representations without human annotation, then transfers those representations to downstream eKYC tasks.

SSL Methods Relevant to eKYC

Method Pretext Task eKYC Application
MAE (Masked Autoencoder) Reconstruct masked image patches Pre-train face/document understanding
SimCLR / MoCo Contrastive: similar images close, different images far Learn face/document features without labels
DINO / DINOv2 Self-distillation with no labels General visual features for any eKYC task
BYOL Predict one augmentation from another Robust feature learning
BEiT Masked visual token prediction Document understanding pre-training

SSL for Face Liveness

graph TD
    A[Large Unlabeled Face Dataset<br/>1M+ face images] --> B[SSL Pre-training<br/>MAE / Contrastive]
    B --> C[Rich Face Representations<br/>Understands face structure, texture, depth]
    C --> D[Fine-tune on Small Labeled Liveness Data<br/>10K-50K labeled images]
    D --> E[Domain-Generalized Liveness Model]

    style C fill:#4051B5,color:#fff
    style E fill:#2E7D32,color:#fff

Why this works for liveness: - Unlabeled face data is abundant (millions available) - Labeled liveness data is scarce and expensive - SSL learns general face features (texture, structure) that transfer to liveness detection

Key Takeaways

Summary

  • SSL enables learning from millions of unlabeled images — critical when labeled data is scarce
  • MAE and contrastive learning are the most relevant SSL methods for eKYC
  • SSL pre-training + liveness fine-tuning is the state-of-the-art approach for domain-generalized liveness
  • DINOv2 provides strong general visual features applicable across all eKYC vision tasks
  • SSL is especially valuable for liveness because labeled attack data is hard to collect