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         author = {Otroshi Shahreza, Hatef and Marcel, S{\'{e}}bastien},
       keywords = {Auto-encoder, Biohashing, Convolutional neural network, deep learning, finger vein recognition, template protection},
       projects = {TRESPASS-ETN},
          title = {Deep Auto-Encoding and Biohashing for Secure Finger Vein Recognition},
      booktitle = {Proceedings of the 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing},
           year = {2021},
       location = {Toronto, Canada},
   organization = {IEEE},
            url = {https://ieeexplore.ieee.org/abstract/document/9414498},
            doi = {10.1109/ICASSP39728.2021.9414498},
       abstract = {Biometric recognition systems relying on finger vein have gained a lot of attention in recent years. Besides security, the privacy of finger vein recognition systems is always a crucial concern. To address the privacy concerns, several biometric template protection (BTP) schemes are introduced in the literature. However, despite providing privacy, BTP algorithms often affect the recognition performance. In this paper, we propose a deep-learning-based approach for secure finger vein recognition. We use a convolutional auto-encoder neural network with a multi-term loss function. In addition to the auto-encoder loss function, we deploy triplet loss for the embedding features. Next, we apply Biohashing to our deep features to generate protected templates. The experimental results indicate that the proposed method achieves superior performance to previous finger vein recognition methods protected with Biohashing. Besides, our proposed method has less execution time and requires less memory.},
            pdf = {https://publications.idiap.ch/attachments/papers/2021/OtroshiShahreza_ICASSP_2021.pdf}