Name
Abstract | ||
|---|---|---|
In this work a methodology for the classification of retinal feature points is applied to a biometric system. This system is based in the extraction of feature points, namely bifurcations and crossovers as biometric pattern. In order to compare a pattern to other from a known individual a matching process takes place between both points sets. That matching task is performed by finding the best geometric transform between sets, i.e. the transform leading to the highest number of matched points. The goal is to reduce the number of explored transforms by introducing the previous characterisation of feature points. This is achieved with a constraint avoiding two differently classified points to match. The empirical reduction of transforms is about 20%. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-04146-4_39 | ICIAP |
Keywords | Field | DocType |
empirical reduction,retinal feature point,feature point,retinal feature points applied,classified point,highest number,previous characterisation,matching process,biometric system,points set,biometric pattern | Computer vision,Pattern recognition,Feature (computer vision),Computer science,Artificial intelligence,Biometrics,Biometric system | Conference |
Volume | ISSN | Citations |
5716 | 0302-9743 | 4 |
PageRank | References | Authors |
0.48 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Calvo | 1 | 47 | 3.72 |
| M Ortega | 2 | 235 | 37.13 |
| Manuel G. Penedo | 3 | 185 | 35.91 |
| Jose Rouco | 4 | 55 | 10.41 |
| Beatriz Remeseiro | 5 | 50 | 11.87 |