U.M. Kelly MSc (Una)

Kelly, U. M. , Nauta, M., Liu, L. , Spreeuwers, L. J. , & Veldhuis, R. N. J. (2023). Worst-Case Morphs using Wasserstein ALI and Improved MIPGAN. https://doi.org/10.48550/arXiv.2310.08371

Bassit, A. , Hahn, F. , Rezgui, Z. , Kelly, U. M. , Veldhuis, R. , & Peter, A. (2023). Template Recovery Attack on Homomorphically Encrypted Biometric Recognition Systems with Unprotected Threshold Comparison. In 2023 IEEE International Joint Conference on Biometrics (IJCB)

Kelly, U. M. , Spreeuwers, L. , & Veldhuis, R. (2023). Exploring Face De-Identification using Latent Spaces. In 2022 IEEE International Joint Conference on Biometrics, IJCB 2022 (pp. 1-7). Article 10007990 (IEEE International Joint Conference on Biometrics (IJCB); Vol. 2022). IEEE. https://doi.org/10.1109/IJCB54206.2022.10007990

Kelly, U. M. , Spreeuwers, L. , & Veldhuis, R. N. J. (2022). Worst-Case Morphs: a Theoretical and a Practical Approach. In A. Bromme, N. Damer, M. Gomez-Barrero, K. Raja, C. Rathgeb, A. F. Sequeira, M. Todisco, & A. Uhl (Eds.), 2022 International Conference of the Biometrics Special Interest Group (BIOSIG) IEEE. https://doi.org/10.1109/BIOSIG55365.2022.9896965

Spreeuwers, L., Schils, M. , Veldhuis, R. , & Kelly, U. (2022). Practical Evaluation of Face Morphing Attack Detection Methods. In C. Rathgeb, R. Tolosana, R. Vera-Rodriguez, & C. Busch (Eds.), Handbook of Digital Face Manipulation and Detection: From DeepFakes to Morphing Attacks (pp. 351-365). (Advances in Computer Vision and Pattern Recognition). Springer. https://doi.org/10.1007/978-3-030-87664-7_16

Kelly, U. M. , Spreeuwers, L. , & Veldhuis, R. (2021). Worst-Case Morphs: a Theoretical and a Practical Approach. ArXiv.org. https://arxiv.org/abs/2111.15416v2

Wang, S. , Kelly, U. M. , & Veldhuis, R. N. J. (2021). Gender Obfuscation through Face Morphing. In Proceedings - 9th International Workshop on Biometrics and Forensics, IWBF 2021 Article 9465088 IEEE. https://doi.org/10.1109/IWBF50991.2021.9465088

Kelly, U. M. , Veldhuis, R. N. J. , & Spreeuwers, L. (2020). Improving deep-learning-based face recognition to increase robustness against morphing attacks. In D. C. Wyld, & J. Zizka (Eds.), 9th International Conference on Signal, Image Processing and Pattern Recognition (SPPR 2020), December 19 ~ 20, 2020, Sydney, Australia (Computer Science & Information Technology; Vol. 10, No. 19). Academy and Industry Research Collaboration Center (AIRCC). https://doi.org/10.5121/csit.2020.101901