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         author = {Otroshi Shahreza, Hatef and Krivokuca, Vedrana and Marcel, S{\'{e}}bastien},
       keywords = {embedding, Face Recognition, face reconstruction, template inversion},
       projects = {TRESPASS-ETN},
          title = {Face Reconstruction from Deep Facial Embeddings using a Convolutional Neural Network},
      booktitle = {Proceedings of the 2022 IEEE International Conference on Image Processing (ICIP)},
           year = {2022},
      publisher = {IEEE},
       location = {Bordeaux, France},
            url = {https://ieeexplore.ieee.org/abstract/document/9897535},
            doi = {10.1109/ICIP46576.2022.9897535},
       abstract = {State-of-the-art (SOTA) face recognition systems generally use deep convolutional neural networks (CNNs) to extract deep features, called embeddings, from face images. The face embeddings are stored in the system’s database and are used for recognition of the enrolled system users. Hence, these features convey important information about the user’s identity, and therefore any attack using the face embeddings jeopardizes the user’s security and privacy. In this paper, we propose a CNN-based structure to reconstruct face images from face embeddings and we train our network with a multi-term loss function. In our experiments, our network is trained to reconstruct face images from SOTA face recognition models (ArcFace and ElasticFace) and we evaluate our face reconstruction network on the MOBIO and LFW datasets. The source code of all the experiments presented in this paper is publicly available so our work can be fully reproduced.},
            pdf = {https://publications.idiap.ch/attachments/papers/2022/OtroshiShahreza_ICIP_2022.pdf}