Name
Title | ||
|---|---|---|
Fingerprint Spoof Detection Using Contrast Enhancement and Convolutional Neural Networks |
Abstract | ||
|---|---|---|
Recently, as biometric technology grows rapidly, the importance of fingerprint spoof detection technique is emerging. In this paper, we propose a technique to detect forged fingerprints using contrast enhancement and Convolutional Neural Networks (CNNs). The proposed method detects the fingerprint spoof by performing contrast enhancement to improve the recognition rate of the fingerprint image, judging whether the sub-block of fingerprint image is falsified through CNNs composed of 6 weight layers and totalizing the result. Our fingerprint spoof detector has a high accuracy of 99.8% on average and has high accuracy even after experimenting with one detector in all datasets. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-981-10-4154-9_39 | Lecture Notes in Electrical Engineering |
Keywords | Field | DocType |
Biometrics,Fingerprint spoof detection,Convolutional neural networks,Multimedia security | Pattern recognition,Convolutional neural network,Computer science,Fingerprint image,Speech recognition,Fingerprint,Artificial intelligence,Biometrics,Detector | Conference |
Volume | ISSN | Citations |
424 | 1876-1100 | 5 |
PageRank | References | Authors |
0.42 | 11 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Han-Ul Jang | 1 | 26 | 5.44 |
| Hak-Yeol Choi | 2 | 13 | 2.22 |
| Dongkyu Kim | 3 | 205 | 22.97 |
| Jeongho Son | 4 | 25 | 6.97 |
| Heung-kyu Lee | 5 | 1016 | 87.53 |