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Synthetic data is emerging as a substitute for authentic data to solve ethical and legal challenges in handling authentic face data. The current models can create real-looking face images of people who do not exist. However, it is a known and sensiti
Externí odkaz:
http://arxiv.org/abs/2311.03970
Explainable Face Recognition is gaining growing attention as the use of the technology is gaining ground in security-critical applications. Understanding why two faces images are matched or not matched by a given face recognition system is important
Externí odkaz:
http://arxiv.org/abs/2304.13409
Autor:
Huber, Marco, Boutros, Fadi, Luu, Anh Thi, Raja, Kiran, Ramachandra, Raghavendra, Damer, Naser, Neto, Pedro C., Gonçalves, Tiago, Sequeira, Ana F., Cardoso, Jaime S., Tremoço, João, Lourenço, Miguel, Serra, Sergio, Cermeño, Eduardo, Ivanovska, Marija, Batagelj, Borut, Kronovšek, Andrej, Peer, Peter, Štruc, Vitomir
This paper presents a summary of the Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data (SYN-MAD) held at the 2022 International Joint Conference on Biometrics (IJCB 2022). The competition attracted a total o
Externí odkaz:
http://arxiv.org/abs/2208.07337
Autor:
Ho, Nhan Thi, Hughes, Steve G., Sekulovich, Rose, Ta, Van Thanh, Nguyen, Thuong Vu, Van Pham, Anh Thi, Luong, Quang Chan, Le Tran, Ly Thi, Van Luu, Anh Thi, Nguyen, Anh Ngoc, Pham, Ha Thai, Nguyen, Van Thu, Berdieva, Dina, Bugarini, Roberto, Liu, Xuexuan, Verhoeven, Carole, Smolenov, Igor, Nguyen, Xuan-Hung
Publikováno v:
NPJ Vaccines; 11/23/2024, Vol. 9 Issue 1, p1-10, 10p
Akademický článek
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