G_render — identity-preserving latent-fingerprint enhancement
g_render2 renders a clean, examiner-friendly fingerprint image from a degraded latent while
preserving identity (ridge angles + minutiae positions). It is a conditional inpainter with a
window-attention U-Net backbone and a hard coverage-gate that forbids inventing ridges where the
input has no evidence — the mechanism that keeps enhancement from destroying identity.
- Backbone:
RenderGenerator= UformerCondGenerator (window spatial self-attention U-Net), 2.94M params - Input: 6-channel conditioning at 256×256 → Output: 1-channel enhanced image
- Trained: supervised
degrade(clean) → cleanon NIST SD302 (1999 rolled + 2000 plain prints) - Benchmark: NIST SD302, 487 latent probes / 1999 rolled gallery
Results (ALL 487 probes)
Image quality — best of all methods tested:
| Method | RQI ↑ | KID ↓ | NFIQ2 ↑ |
|---|---|---|---|
| g_render2 (this) | 0.811 | 0.165 | 17.1 |
| clan_efsroi | 0.672 | 0.197 | 11.1 |
| FLARE | 0.624 | 0.165 | 14.2 |
| FingerGAN | 0.393 | 0.264 | 36.8 |
| raw latent | 0.290 | 0.263 | 8.3 |
Identity (FingerNet → DMD dense-descriptor matcher):
| Method | Rank-1 ↑ | AUC ↑ | EER ↓ |
|---|---|---|---|
| g_render2 + classic_lan post-proc | 0.610 | 0.799 | 0.232 |
| raw latent | 0.608 | 0.796 | 0.244 |
| g_render2 | 0.600 | 0.788 | 0.261 |
| FLARE | 0.559 | 0.749 | 0.296 |
| FingerGAN | 0.353 | 0.722 | 0.335 |
g_render2 is the only deep enhancer that reaches best-in-class image quality while keeping matcher
identity at or above the raw latent (all published deep enhancers we tested fall below the raw
latent on the matcher). Adding a 0-param analytic classic_lan post-process nudges identity slightly
above the raw latent (Rank-1 0.610 ≥ 0.608).
Usage
python inference.py input_latent.png output_enhanced.png \
[--roi roi_mask.png] [--minu minutiae_xy.txt] [--device cuda]
import torch
from g_render.models.render_generator import RenderGenerator
from inference import enhance
model = RenderGenerator(in_ch=6).eval()
model.load_state_dict(torch.load("pytorch_model.bin", map_location="cpu"), strict=False)
enhanced = enhance(model, "input_latent.png", roi_path=None, minu=None, device="cpu") # PIL.Image
enhanced.save("output.png")
Input conditioning (6 channels, built by inference.build_cond)
| ch | content |
|---|---|
| 0 | input image (classic_lan-normalized) — the only raw-pixel channel |
| 1–2 | robust orientation field [cos2θ, sin2θ] · coverage |
| 3 | coverage / ROI mask |
| 4 | ridge-frequency map · coverage |
| 5 | minutiae Gaussian heatmap |
--roi (foreground mask) and --minu (minutiae) are optional: without them the ROI is derived
from ridge coherence and the minutiae channel is zero. A curated ROI (e.g. a learned segmenter or an
examiner-marked region) gives the cleanest result — the coverage-gate blanks everything outside it.
Files
pytorch_model.bin (weights) · config.json · inference.py (full pipeline) · g_render/
(self-contained model code, no external repo needed).
Limitations
- Does not add identity information (data-processing-inequality ceiling): it preserves the
latent's identity, it does not exceed it. For the strongest automated matching, the analytic
classic_lanbaseline still leads (Rank-1 0.706); useg_render2when a clean, realistic image that keeps identity is the goal (human examiners, visualization). - Trained/evaluated on 500-ppi NIST SD302 latents; other sensors/resolutions may need re-tuning.
- Minutiae positions are jittered slightly by the re-render (weaker on the position-only Bozorth3 matcher than on the dense DMD matcher).
Citation / provenance
Derived from the FDC-LFE project. Bench protocol: FingerNet→DMD / FingerNet→Bozorth3 on NIST SD302
(487 probes / 1999 gallery). Post-processing baseline classic_lan = local adaptive contrast norm
(target_std 0.18, gain∈[0.5,4], k=13). License follows the NIST SD302 data terms — verify before use.