Name
Title | ||
|---|---|---|
Towards Improving the NIST Fingerprint Image Quality (NFIQ) Algorithm (Extended Version) |
Abstract | ||
|---|---|---|
The NIST Fingerprint Image Quality (NFIQ) algorithm has become a standard
method to assess fingerprint image quality. However, in many applications a
more accurate and reliable assessment is desirable. In this publication, we
report on our efforts to optimize the NFIQ algorithm by a re-training of the
underlying neural network based on a large fingerprint image database. Although
we only achieved a marginal improvement, our work has revealed several areas
for potential optimization. |
Year | Venue | Keywords |
|---|---|---|
2010 | Clinical Orthopaedics and Related Research | image quality,neural network |
Field | DocType | Volume |
Data mining,Computer science,Fingerprint image,Algorithm,NIST,Artificial neural network | Journal | abs/1008.0 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Johannes Merkle | 1 | 75 | 12.14 |
| Michael Schwaiger | 2 | 17 | 2.88 |
| Oliver Bausinger | 3 | 4 | 1.86 |
| Marco Breitenstein | 4 | 4 | 1.20 |
| Kristina Elwart | 5 | 0 | 0.34 |
| Markus Nuppeney | 6 | 0 | 0.68 |