Name
Abstract | ||
|---|---|---|
Deep learning has recently achieved impressive performance in the area of biometric recognition. The technology of finger vein recognition possesses better anti forgery performance and identification stability in collecting and certificating information of human bodies. The traditional finger vein recognition method is mainly based on template matching or whole feature recognition, suffering from light instability of the acquisition equipment which leads to low robustness. In this paper, we adapt a finger vein recognition algorithm using feature block fusion and deep belief network (FBF-DBN) and a convolutional neural network (CNN), then we combine these two network systems to judge the performance of our system. Meanwhile, we improve deep network input by using feature points set in vein images, effectively reducing the time in learning and detection, meeting the practical needs of biometric recognition specifically applied to embedded equipment. The experiment results showed that FBF-DBN and CNN algorithm present better recognition performance and faster speed. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1504/IJES.2017.10005714 | INTERNATIONAL JOURNAL OF EMBEDDED SYSTEMS |
Keywords | Field | DocType |
feature block fusion, FBF, deep belief network, DBN, convolution neural network, finger vein recognition | Template matching,Pattern recognition,Convolutional neural network,Computer science,Feature recognition,Deep belief network,Algorithm,Robustness (computer science),Artificial intelligence,Deep learning,Biometrics,Finger vein recognition | Journal |
Volume | Issue | ISSN |
9 | 3 | 1741-1068 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cheng Chen | 1 | 97 | 32.69 |
| Zhendong Wu | 2 | 91 | 14.16 |
| Jianwu Zhang | 3 | 18 | 7.11 |
| Ping Li | 4 | 78 | 14.22 |
| Freeha Azmat | 5 | 9 | 2.28 |