Abstract
We study the finger vein (FV) sensor model identification task using a deep learning approach. So far, for this biometric modality, only correlation-based PRNU and texture descriptor-based methods have been applied. We employ five prominent CNN architectures covering a wide range of CNN family models, including VGG16, ResNet, and the Xception model. In addition, a novel architecture termed FV2021 is proposed in this work, which excels by its compactness and a low number of parameters to be trained. Original samples, as well as the region of interest data from eight publicly accessible FV datasets, are used in experimentation. An excellent sensor identification AUC-ROC score of 1.0 for patches of uncropped samples and 0.9997 for ROI samples have been achieved. The comparison with former methods shows that the CNN-based approach is superior and improved the results.
| Original language | English |
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| Title of host publication | 2021 IEEE International Workshop on Biometrics and Forensics (IWBF) |
| Publisher | IEEE |
| Pages | 1-6 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781728195568 |
| ISBN (Print) | 9781728195568 |
| DOIs | |
| Publication status | Published - 7 May 2021 |
| Event | 2021 IEEE International Workshop on Biometrics and Forensics (IWBF) - Rome, Italy Duration: 6 May 2021 → 7 May 2021 |
Publication series
| Name | Proceedings - 9th International Workshop on Biometrics and Forensics, IWBF 2021 |
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Conference
| Conference | 2021 IEEE International Workshop on Biometrics and Forensics (IWBF) |
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| Period | 6/05/21 → 7/05/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
Keywords
- Deep learning
- Biometrics (access control)
- Databases
- Veins
- Biological system modeling
- Forensics
- Conferences
- Xception
- sensor identification
- CNN
- texture descriptor
- PRNU
- classification
- image origin
- Residual Network
- finger vein
Fields of Science and Technology Classification 2012
- 102 Computer Sciences