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• W2973423935 abstract "FaceFace is the most accessible biometric modality which can be used for identity verification in mobile phoneMobile phone applications, and it is vulnerable to many different presentation attacksPresentation attack, such as using a printed faceFace/digital screen faceFace to access the mobile phoneMobile phone. Presentation attackPresentation attack detection is a very critical step before feeding the faceFace image to faceFace recognition systems. In this chapter, we introduce a novel two-stream CNN-based approach for the presentation attackPresentation attack detection, by extracting the patch-based features and holistic depthDepth maps from the faceFace images. We also introduce a two-stream CNN v2 with model optimization, compression and a strategy of continuous updating. The CNN v2 shows great performances of both generalization and efficiency. Extensive experiments are conducted on the challenging databases (CASIA-FASD, MSU-USSA, replay attack, OULU-NPU, and SiW), with comparison to the state of the art." @default.
• W2973423935 created "2019-09-26" @default.
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• W2973423935 date "2019-01-01" @default.
• W2973423935 modified "2023-09-23" @default.
• W2973423935 title "Presentation Attack Detection for Face in Mobile Phones" @default.
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• W2973423935 doi "https://doi.org/10.1007/978-3-030-26972-2_8" @default.
• W2973423935 hasPublicationYear "2019" @default.
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