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@ARTICLE{OtroshiShahreza_TBIOM_2021,
         author = {Otroshi Shahreza, Hatef and Marcel, S{\'{e}}bastien},
       keywords = {Auto-encoder, Biohashing, Biometrics, Deep neural network, Finger vein, template protection, vascular biometrics},
       projects = {TRESPASS-ETN},
          title = {Towards Protecting and Enhancing Vascular Biometric Recognition methods via Biohashing and Deep Neural Networks},
        journal = {IEEE Transactions on Biometrics, Behavior, and Identity Science},
           year = {2021},
            url = {https://ieeexplore.ieee.org/document/9419051},
            doi = {10.1109/TBIOM.2021.3076444},
       abstract = {Biometric template protection has been a crucial concern in biometric recognition systems. This is because biometric characteristics are irreplaceable, so the compromised templates can also be used in other applications. On the other side, deploying template protection algorithms often affects the performance of biometric systems. In this paper, we consider both raw and pre-processed finger vein images and propose a novel deep-learning-based framework to protect biometric templates and enhance recognition performance. We use a deep convolutional auto-encoder structure to reduce the dimension of the feature space, and then secure templates by applying the Biohashing algorithm on the features extracted at the bottleneck layer of our auto-encoder. The experimental results indicate that the protected templates through our framework achieve superior performance than Biohash protected templates of the raw features in the normal scenario. In the stolen scenario, where the Biohashing key is stolen, our model yields far better performance than Biohashing of raw features extracted by previous recognition methods.  
We also evaluate the generalization of our proposed framework on other vascular biometric modalities. It is worth mentioning that we provide an open-source implementation of our framework so that other researchers can verify our findings and build upon our work.},
            pdf = {https://publications.idiap.ch/attachments/papers/2021/OtroshiShahreza_TBIOM_2021.pdf}
}