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An Experimental Evaluation of Recent Face Recognition Losses for Deepfake Detection., , , , and . ICPR, page 9827-9834. IEEE, (2020)Hearing and Seeing Abnormality: Self-Supervised Audio-Visual Mutual Learning for Deepfake Detection., , and . ICASSP, page 1-5. IEEE, (2023)Heterogeneous Federated Learning Through Multi-Branch Network., , , , and . ICME, page 1-6. IEEE, (2021)Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition., , , , , , and . CoRR, (2018)Unconstrained face verification using deep CNN features., , and . WACV, page 1-9. IEEE Computer Society, (2016)Frontal to profile face verification in the wild., , , , , and . WACV, page 1-9. IEEE Computer Society, (2016)Towards the design of an end-to-end automated system for image and video-based recognition., , , , , , and . ITA, page 1-7. IEEE, (2016)Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks., , , , , , , and . Int. J. Comput. Vis., 126 (2-4): 272-291 (2018)Lightweight Face Anti-Spoofing Network for Telehealth Applications., , , , , , and . IEEE J. Biomed. Health Informatics, 26 (5): 1987-1996 (2022)Robust Representations for unconstrained Face Recognition and its Applications.. University of Maryland, College Park, MD, USA, (2016)base-search.net (ftunivmaryland:oai:drum.lib.umd.edu:1903/19013).