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    • 2. 发明专利
    • ENHANCED ACCESS CONTROL
    • AU2022202737A1
    • 2022-11-24
    • AU2022202737
    • 2022-04-26
    • DAON ENTERPRISES LTD
    • AHERN JAMES
    • G06V40/40G06F21/32G07C9/25H04L9/32
    • A method for enhanced access control is provided that includes the steps of displaying buttons, by an electronic device, where each button corresponds to a different service. Moreover, the method includes receiving, by the electronic device, input regarding a selected service, transmitting at least one credential for the selected service to a computer, and capturing, by a camera in communication with the computer, facial image data of a user. The method also includes determining whether the facial image data was taken of a live person. In response to determining the facial image data was taken of a live person, a verification transaction is conducted based on the at least one credential and facial image data. In response to verifying the identity of the user as true, the user is granted access to the selected service. DISPLAY BUTTONS THAT EACH CORRESPOND TO A DIFFERENT -S33 RECEIVE INPUT REGARDING S34 SELECTED SERVICE DISPLAY TOKEN CORRESPONDING TO SELECTED SERVICE AND V PRESENTTOKENTOSCANNER TRANSMIT CREDENTIAL(S) FOR S36 SELECTED SERVICE CAPTURE FACIAL IMAGE DATA OF USER S37 NO MAGE DATA TAKE NO USER IDENTITY r fS39 TRANSMIT SIGNAL, DISCARD USER CREDENTIAL AND FACIAL _S42 ACCESS IS IMAGE DATA, GRANT USER DENIED ACCESS TO SERVICE (END S40
    • 7. 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.