.png){kind=icon}
.png){kind=icon}
Return
Dynamic registration selection for fingerprint verification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2006.02.020.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Information fusion is a powerful approach to increasing the accuracy of biometric authentication systems, and is currently an active area of research. The majority of studies focus on combining the results from multiple verification systems at the match score level using either a classification or combination scheme. However, there are advantages to performing the fusion at an earlier stage of processing. Fingerprint registration involves finding the translation and rotation parameters that align two fingerprints; a challenging problem that can be approached in a number of ways. The fusion of fingerprint alignment algorithms is introduced in the form of dynamic registration selection. A Bayesian statistical framework is used to select the most probable alignment produced by competing algorithms. The results of the proposed technique are tested on multiple FVC 2002 databases, and are shown to outperform methods based on match score combination. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keywords:
biometrics
fingerprint verification
registration
classifier combination
Bayesian classifier
fingerprint alignment
fingerprint verification competition
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
No organization information available