Name
Abstract | ||
|---|---|---|
The public demand for personal safety is increasing rapidly. Fingerprint features as the most commonly used bio-signature need to improve their safety continuously. The third level features of fingerprint (especially the sweat pores) can be added to the automatic fingerprint recognition system to increase the accuracy of fingerprint identification in a variety of environments. Due to perspiration activities, the shape and size sweat of pores are varying spatially and temporally. Extraction of fingerprint pores is both critical and challenging. In this paper, we adapt a novel fully convolutional neural network called U-net for ridges and sweat pores extraction. The PolyU High-Resolution-Fingerprint (HRF) database is used for testing of the proposed method. The results show the validity of the proposed method. With the majority of the pores correctly extracted, the proposed method can serve for fingerprint recognition using Level 3 features. |
Year | Venue | Field |
|---|---|---|
2017 | CCBR | Pattern recognition,Computer science,Fingerprint recognition,Convolutional neural network,Fingerprint,Artificial intelligence |
DocType | Citations | PageRank |
Conference | 2 | 0.39 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haixia Wang | 1 | 132 | 27.85 |
| Xicheng Yang | 2 | 2 | 0.39 |
| Lingtao Ma | 3 | 2 | 0.39 |
| Ronghua Liang | 4 | 376 | 42.60 |