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    • 1. 发明专利
    • Anti-spoofing
    • GB2588538A
    • 2021-04-28
    • GB202018751
    • 2019-12-04
    • YOTI HOLDING LTD
    • SYMEON NIKITIDISFRANCISCO ANGEL GARCIA RODRIGUEZERLEND DAVIDSONSAMUEL NEUGBER
    • G06K9/00
    • A method of configuring an anti-spoofing system to detect if a spoofing attack has been attempted, in which an image processing component of the anti-spoofing system is trained to process 2D verification images according to a set of image processing parameters, in order to extract depth information from the 2D verification images. The configured anti-spoofing system comprises an anti-spoofing component which uses an output from the processing of a 2D verification image by the image processing component to determine whether an entity captured in that image corresponds to an actual human or a spoofing entity. The image processing parameters are learned during the training from a training set of captured 3D training images of both actual humans and spoofing entities, each 3D training image comprising 2D image data and corresponding depth data, by: processing the 2D image data of each 3D training image according to the image processing parameters, so as to compute an image processing output for comparison with the corresponding depth data of that 3D image; and adapting the image processing parameters in order to match the image processing outputs to the corresponding depth data, thereby training the image processing component to extract depth information from 2D verification images.
    • 2. 发明专利
    • Anti-spoofing
    • GB2607496A
    • 2022-12-07
    • GB202211582
    • 2019-12-04
    • YOTI HOLDING LTD
    • SYMEON NIKITIDISFRANCISCO ANGEL GARCIA RODRIGUEZERLEND DAVIDSONSAMUEL NEUGBER
    • G06V40/40G06V40/16
    • An anti-spoofing system 602 is disclosed which comprises a depth estimation component, a global anti-spoofing classifier, and a patch-based anti-spoofing classifier. The depth estimation component receives a 2D verification image (206) and extracts estimated depth information therefrom. The global anti-spoofing classifier 504 uses the extracted depth information to classify the 2D verification image in relation to real (actual humans) and spoofing classes, and thereby assigns a global classification value to the whole of the image. The patch-based anti-spoofing classifier 1102a,b classifies each image patch of the 2D verification image in relation to the real and anti-spoofing classes, and thereby assigns a local classification value to each image patch. The system combines 1104 the global and local classification values to determine whether an entity captured in the 2D verification image corresponds to an actual human or a spoofing entity. The patched-based classifier could employ convolutional neural networks 110a,b to define patches. The methods could be used to detect mask, cut-out, replay or print attacks.