Cross-Quality Face Matching
Definition
Cross-quality face matching addresses the challenge of comparing a high-resolution selfie with a low-quality ID photo. ID document photos are often small (100-200px), printed, faded, and captured years ago — creating a significant quality gap with the live selfie.
The Quality Gap
| Attribute |
ID Document Photo |
Live Selfie |
| Resolution |
100-200px face |
300-800px face |
| Source |
Printed on card, scanned |
Direct camera capture |
| Artifacts |
Print dots, moiré, hologram reflections |
Camera noise, compression |
| Color |
Faded, color-shifted |
True color |
| Lighting |
Studio flash (original), ambient (scan) |
Variable ambient |
| Compression |
Multiple generations of compression |
Single JPEG |
AdaFace — Quality-Adaptive Recognition
| Aspect |
Details |
| Key insight |
Image quality affects embedding reliability — hard samples from low quality should have less gradient impact |
| Quality proxy |
Feature norm (‖f‖) correlates with image quality |
| Adaptive margin |
High-quality images get harder margin (more discriminative), low-quality get softer margin (more forgiving) |
| Result |
Significantly better cross-quality matching than ArcFace |
AdaFace vs ArcFace on Quality-Degraded Benchmarks
| Benchmark |
ArcFace-R100 |
AdaFace-R100 |
Improvement |
| IJB-C (TAR@FAR=1e-4) |
96.02% |
97.39% |
+1.37% |
| IJB-S (Surveillance) |
58.29% |
63.41% |
+5.12% |
| TinyFace |
63.89% |
72.02% |
+8.13% |
The improvement is most dramatic on low-quality benchmarks (TinyFace, IJB-S).
Other Quality-Aware Approaches
| Method |
Approach |
| SER-FIQ |
Estimate quality from embedding robustness (self-supervised) |
| MagFace |
Embedding magnitude indicates quality — penalize low-quality equally |
| FaceQnet |
Dedicated quality estimation network trained on verification performance |
| Super-resolution |
Enhance ID photo resolution before embedding (GFPGAN, CodeFormer) |
Key Takeaways
Summary
- Cross-quality matching is a core eKYC challenge — ID photos are 3-10x lower quality than selfies
- AdaFace specifically solves this with quality-adaptive margin — best choice for eKYC
- Super-resolution (GFPGAN) can enhance ID photos but adds computation and may introduce artifacts
- Quality-aware threshold adjustment provides additional robustness
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