Name
Abstract | ||
|---|---|---|
As an emerging biometric modality, finger vein recognition has received considerable attentions. However, recent studies have shown that finger vein biometrics is vulnerable to presentation attacks, i.e. printed versions of authorized individuals’ finger veins could be used to gain access to facilities or services. In this paper, we have designed a specific shallow convolutional neural network (CNN) for finger vein presentation attack detection (PAD), which is called as FPNet for short. The proposed FPNet has been evaluated on a public-database and an intra-database. Lots of h × h patches have been extracted from vein images with a stride s for dataset augmentation and then used to train our networks without any pre-trained model. For further improving models’ generalizability and robustness, training patches of two databases have been mixed together and our best model has achieved an accuracy of 100% on both test datasets, clearly outperforming state-of-the-art methods. |
Year | Venue | Field |
|---|---|---|
2017 | CCBR | Generalizability theory,STRIDE,Convolutional neural network,Computer science,Vein,Speech recognition,Robustness (computer science),Biometrics,Finger vein recognition |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
12 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinwei Qiu | 1 | 4 | 0.78 |
| Senping Tian | 2 | 15 | 8.70 |
| Wenxiong Kang | 3 | 102 | 17.58 |
| Wei Jia | 4 | 1025 | 53.39 |
| Qiuxia Wu | 5 | 103 | 9.25 |