System for determining embedding from multiple inputs

    公开(公告)号:US11670104B1

    公开(公告)日:2023-06-06

    申请号:US17097707

    申请日:2020-11-13

    CPC classification number: G06V40/11 G06V10/469 G06V40/13 G06V40/117

    Abstract: A scanner acquires a set of images of a hand of a user to facilitate identification. These images may vary, due to changes in relative position, pose, lighting, obscuring objects such as a sleeve, and so forth. A first neural network determines output data comprising a spatial mask and a feature map for individual images in the set. The output data for two or more images is combined to provide aggregate data that is representative of the two or more images. The aggregate data may then be processed using a second neural network, such as convolutional neural network, to determine an embedding vector. The embedding vector may be stored and associated with a user account. At a later time, images acquired from the scanner may be processed to produce an embedding vector that is compared to the stored embedding vector to identify a user at the scanner.

    System to reduce data retention
    3.
    发明授权

    公开(公告)号:US12086225B1

    公开(公告)日:2024-09-10

    申请号:US17448437

    申请日:2021-09-22

    CPC classification number: G06F21/32 G06F18/213 G06F18/214 G06F21/6245

    Abstract: An image of at least a portion of a user during enrollment to a biometric identification system is acquired and processed with a first model to determine a first embedding that is representative of features in that image in a first embedding space. The first embedding may be stored for later comparison to identify the user, while the image is not stored. A second model that uses a second embedding space may be later developed. A transformer is trained to accept as input an embedding from the first model and produce as output an embedding consistent with the second embedding space. The previously stored first embedding may be converted to a second embedding in a second embedding space using the transformer. As a result, new embedding models may be implemented without requiring storage of user images for later reprocessing with the new models or requiring re-enrollment by users.

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