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• W3209649633 abstract "3D mask presentation attack detection (PAD) is a long standing challenge in face anti-spoofing due to the high fidelity of attack artifacts and a limited number of samples available for training and evaluation. With the recent release of the large-scale and diverse CASIA-SURF HiFiMask dataset [19], it now becomes possible to address 3D mask PAD with deep neural networks. This paper introduces a new one-shot method for 3D mask PAD that extracts fine-grained information from appropriate parts of the human face and uses it to identify subtle differences between real and fake samples. The proposed method achieves state-of-the-art results of 3% ACER on the CASIA-SURF HiFiMask test set." @default.
• W3209649633 created "2021-11-08" @default.
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• W3209649633 date "2021-10-01" @default.
• W3209649633 modified "2023-09-27" @default.
• W3209649633 title "3D mask presentation attack detection via high resolution face parts" @default.
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• W3209649633 doi "https://doi.org/10.1109/iccvw54120.2021.00100" @default.
• W3209649633 hasPublicationYear "2021" @default.
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