Name
Abstract | ||
|---|---|---|
In this paper, we propose an automatic approach to measure the minutiae quality. When image of 500 dpi is captured, immediately the enhancement, thinning and minutiae extraction processes are executed. The basic idea is to detect the spatial β0 – Connected minutiae cluster using the Euclidean distance and quantify the number of element for each group. In general, we observe that more than five element in a group is a clue to mark all points in the cluster as bad minutiae. We divide the image in block of 20 x 20 pixels. If one block contains bad minutiae it is mark as a bad block. The goodness quality index is calculated as the proportion of bad blocks respect to the number of total blocks. The proposed index was tested on the FVC2000 fingerprint image database. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11578079_60 | CIARP |
Keywords | Field | DocType |
euclidean distance,indexation | Computer vision,Pattern recognition,Fingerprint recognition,Computer science,Minutiae,Euclidean distance,Block code,Image processing,Fingerprint image,Fingerprint,Artificial intelligence,Pixel | Conference |
Volume | ISSN | ISBN |
3773 | 0302-9743 | 3-540-29850-9 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edel Garcia-Reyes | 1 | 95 | 12.84 |
| José Rodríguez | 2 | 3 | 1.11 |
| Mabel Iglesias Ham | 3 | 22 | 4.91 |