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afis

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AFIS library: minutiae extraction (classical + LEADER ONNX), matching, and quality

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Direct dependencies7From release 1.2.7

Last observed20 Oct, 21:07 UTC

README

Version 1.2.7 documentation

afis

AFIS toolkit for fingerprint minutiae extraction, matching, and quality assessment.

Install

pip install afis
# or
uv add afis

From source (includes the optional native NBIS library):

make install          # uv sync + make native

Quickstart

from afis import NbisExtractor, MindtctExtractor, SafisExtractor, LeaderExtractor, compute_nfiq2

nbis = NbisExtractor()
a = nbis.extract_minutiae("finger_a.png")
b = nbis.extract_minutiae("finger_b.png")
print(nbis.match(a, b))

# Mix extractor / matcher
proc = NbisExtractor(extractor="gabor", matcher="mcc")
t = proc.extract_minutiae("finger_a.png")

leader = LeaderExtractor()
lt = leader.extract_minutiae("finger_a.png")

q = compute_nfiq2(image)  # NFIQ2-style 0–100
print(q.score)

Extractors

Name	Kind
`nbis_mindtct`	Native NBIS MINDTCT / LFS V2
`mindtct`	Pure-Python MINDTCT
`safis`	SourceAFIS minutiae path
`gabor`	Gabor enhance + crossing-number
`stft`	STFT enhance + crossing-number
`leader`	LEADER ONNX

Matchers

Name	Kind
`nbis_bozorth3`	Native Bozorth3
`bozorth3`	Pure-Python Bozorth3
`safis`	SourceAFIS edge crawl
`mcc`	Minutia Cylinder-Code
`geometric`	Rigid geometric

Quality (NFIQ2)

compute_nfiq2(image) returns a 0–100 score (ISO/IEC 29794-4 / NFIQ 2 style).

Benchmark (FVC2004)

FVC B-set protocol on DB1–4_B (10×8 images/DB): genuine = all same-finger impression pairs; impostor = first impressions across fingers. Metrics are macro-averaged over the four DBs.

uv run python examples/benchmark.py \
  --root /path/to/fingerprint_datasets \
  --workers 10

Subset / custom combos:

uv run python examples/benchmark.py \
  --dbs DB1_B DB3_B --max-fingers 8 --max-imps 4 \
  --extractors nbis_mindtct leader \
  --matchers nbis_bozorth3 mcc geometric \
  --workers 8

Results (FVC2004 DB1–4_B, full)

Extractor	Matcher	EER %	FMR100 %	verify ms	extract ms	match ms
leader	geometric	6.78	13.48	1175.9	1167.0	8.91
nbis_mindtct	nbis_bozorth3	10.10	25.45	35.6	20.9	14.75
leader	nbis_bozorth3	11.82	17.59	1170.3	1167.0	3.31
leader	mcc	14.03	24.91	1169.4	1167.0	2.41
nbis_mindtct	mcc	15.52	26.79	25.7	20.9	4.83
nbis_mindtct	geometric	21.34	46.70	31.9	20.9	11.08

Presets

Preset	Extractor	Matcher	EER %	verify ms
fast	nbis_mindtct	mcc	15.52	25.7
balanced	leader	nbis_bozorth3	11.82	1170.3
deep	leader	geometric	6.78	1175.9

verify_ms ≈ mean extract + mean match (1:1 verify proxy).

CLI

afis extract finger.png finger.xyt
afis extract finger.png out.xyt --extractor mindtct
afis extract finger.png out.xyt --extractor leader
afis match a.xyt b.xyt --method bozorth3 --height 480
afis match a.xyt b.xyt --method mcc
afis quality finger.png

Develop

make install
make test
make build

References

NIST NBIS — MINDTCT / LFS V2 / Bozorth3. NIST Biometric Image Software
NFIQ 2 / ISO/IEC 29794-4 — usnistgov/NFIQ2
SourceAFIS — algorithm
MCC — Cappelli, Ferrara & Maltoni (2010). IEEE TPAMI. https://doi.org/10.1109/TPAMI.2010.52
LEADER — pyfing
Gabor enhancement — Hong, Wan & Jain (1998). IEEE TPAMI
ISO/IEC 19794-2 — Finger minutiae data interchange
FVC / FVC-onGoing — Verification benchmarks (EER, FMR, FNMR)

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Versions 1

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Version	Published	Direct dependencies	Open version
1.2.7	19 Aug 2026	7

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ashokpant/afis

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