Vision Transformers for eKYC¶
Definition¶
Vision Transformers (ViTs) apply the transformer architecture (self-attention) to image processing — capturing global context that CNNs struggle with, at the cost of higher compute requirements.
Key ViT Models for eKYC¶
| Model | Params | Key Feature | eKYC Application |
|---|---|---|---|
| ViT-Small | 22M | Standard ViT | Face recognition, liveness |
| DeiT-Small | 22M | Distilled, data-efficient | When training data is limited |
| Swin-T | 28M | Shifted windows for efficiency | Document understanding |
| ViT-Base | 86M | Larger capacity | Server-side processing |
| DINOv2 | Various | Self-supervised pre-training | General feature backbone |
ViT vs CNN for eKYC¶
| Aspect | CNN | ViT |
|---|---|---|
| Local features | Strong (convolution is local) | Weaker (global attention) |
| Global context | Weak (limited receptive field) | Strong (full image attention) |
| Mobile speed | Fast | 2-5x slower |
| Data efficiency | Better with small datasets | Needs more data (or pre-training) |
| Liveness | CDCN captures fine texture | ViT captures global layout cues |
| Document understanding | Limited layout understanding | Excellent (LayoutLMv3 is transformer) |
Key Takeaways¶
Summary
- ViTs capture global context — valuable for liveness (full-image analysis) and documents (layout understanding)
- LayoutLMv3 (transformer) dominates document understanding
- For face recognition, ViTs match but don't significantly beat CNNs — CNNs remain preferred for speed
- DINOv2 provides excellent general features for any eKYC task via self-supervised pre-training
- Hybrid (CNN early layers + transformer later layers) may be optimal