Name
Abstract | ||
|---|---|---|
Fingerprint image quality assessment is a very important task as the performance of automatic fingerprint identification systems relies heavily on the quality of fingerprint images. Existing methods have made many efforts to find out more appropriate solutions, but most of them operate either on full regions of a fingerprint image, or on local areas. Unlike previous methods, we divide fingerprint images into blocks, and define the quality levels of the blocks according to the minutiae on them and their ridge orientation certainty. With the manually prepared quality-specific fingerprint image blocks, we train a convolutional neural network (CNN) to fulfill end-to-end quality prediction for fingerprint image blocks. The global quality of a fingerprint image can be obtained by fusing the quality levels of its blocks. We evaluate the proposed method on FVC2002 DB1A and FVC2002 DB2A. Experimental results show that the proposed method can effectively distinguish good quality fingerprints from bad ones, and ensure high fingerprint recognition accuracy. |
Year | Venue | Field |
|---|---|---|
2017 | CCBR | Pattern recognition,Minutiae,Fingerprint recognition,Computer science,Convolutional neural network,Fingerprint image,Fingerprint,Artificial intelligence |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianqi Yan | 1 | 0 | 0.34 |
| Xiaowei Dai | 2 | 0 | 0.34 |
| Qijun Zhao | 3 | 419 | 38.37 |
| Feng Liu | 4 | 13 | 4.75 |