Name
Abstract | ||
|---|---|---|
The fingerprint image quality is a key factor on the match results since it will cause spurious and missed minutiae when matching with the low quality images. It is important to estimate the image quality to guide the feature extraction and matching. In this paper we investigate the specifications that can reflect the image quality such as orientation coherence, core position and so on. We define a quasi core as a stable point to examine the validity of the captured position. We apply the idea of penalty function in the optimization theory to combine the specifications to get a quality score. The method is robust since it investigates the quality specifications entirely. The testing results on FVC database are given to verify the feasibility and effectiveness. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-25944-9_53 | ICIC (2) |
Keywords | Field | DocType |
core position,systematic algorithm,feature extraction,quality score,image quality,fingerprint image quality assessment,quality specification,fingerprint image quality,quasi core,key factor,fvc database,low quality image,penalty function | Data mining,Quality Score,Pattern recognition,Minutiae,Computer science,Fingerprint image,Image quality,Feature extraction,Coherence (physics),Artificial intelligence,Spurious relationship,Penalty method | Conference |
Volume | ISSN | Citations |
6839 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min Wu | 1 | 251 | 50.43 |
| A Yong | 2 | 15 | 0.86 |
| Tong Zhao | 3 | 14 | 7.30 |
| Tiande Guo | 4 | 67 | 7.35 |