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FRCSyn Challenge at WACV 2024: Face Recognition Challenge in the Era of Synthetic Data., , , , , , , , , and 37 other author(s). CoRR, (2023)Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data., , , , , , , , , and 48 other author(s). CoRR, (2024)Real-Time Driver State Monitoring Using a CNN Based Spatio-Temporal Approach., , , and . ITSC, page 3236-3242. IEEE, (2019)Using High Performance Computing in Vehicles to Create Image Datasets for Deep Learning., , and . MIPRO, page 1001-1006. IEEE, (2019)SDFR: Synthetic Data for Face Recognition Competition., , , , , , , , , and 18 other author(s). CoRR, (2024)FRCSyn-onGoing: Benchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems., , , , , , , , , and 37 other author(s). Inf. Fusion, (2024)An anomaly detection approach for backdoored neural networks: face recognition as a case study., and . BIOSIG, volume P-329 of LNI, page 80-88. IEEE / Gesellschaft für Informatik e.V., (2022)DMD: A Large-Scale Multi-modal Driver Monitoring Dataset for Attention and Alertness Analysis., , , , , , , and . ECCV Workshops (4), volume 12538 of Lecture Notes in Computer Science, page 387-405. Springer, (2020)FRCSyn Challenge at WACV 2024: Face Recognition Challenge in the Era of Synthetic Data., , , , , , , , , and 37 other author(s). WACV (Workshops), page 892-901. IEEE, (2024)