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    Direction estimation device, direction estimation method, and storage medium
    2.
    发明授权
    Direction estimation device, direction estimation method, and storage medium 有权

    公开(公告)号：US12125315B2

    公开(公告)日：2024-10-22

    申请号：US17680844

    申请日：2022-02-25

    Applicant: NEC Corporation

    Inventor： Yusuke Morishita

    IPC: G06V40/20 ,  G06T7/73 ,  G06V10/25 ,  G06V40/16 ,  G06V40/18

    CPC classification number: G06V40/171 ,  G06T7/73 ,  G06V10/25 ,  G06V40/168 ,  G06V40/197 ,  G06T2207/30201

    Abstract: An estimation device according to one aspect of the present disclosure includes: at least one memory storing a set of instructions; and at least one processor configured to execute the set of instructions to: generate a plurality of extraction regions by adding a perturbation to an extraction region of a partial image determined based on positions of feature points extracted from a face image; estimate a plurality of directions of at least one of a face and a line of sight and a reliability of each of the plurality of directions based on a plurality of partial images in the plurality of extraction regions of the face image; and calculate an integrated direction obtained by integrating the plurality of directions based on the estimated reliability.

    METHOD AND APPARATUS FOR CREATING ENCODED DATA AND USE OF SAME FOR IDENTITY VERIFICATION
    3.
    发明公开
    METHOD AND APPARATUS FOR CREATING ENCODED DATA AND USE OF SAME FOR IDENTITY VERIFICATION 审中-公开

    公开(公告)号：US20240346852A1

    公开(公告)日：2024-10-17

    申请号：US18627393

    申请日：2024-04-04

    Applicant: FaceTec, Inc.

    Inventor： Kevin Alan Tussy

    IPC: G06V40/50 ,  G06K7/10 ,  G06K7/14 ,  G06V10/74 ,  G06V30/40 ,  G06V40/16 ,  G06V40/40

    CPC classification number: G06V40/53 ,  G06K7/10722 ,  G06K7/1417 ,  G06V10/761 ,  G06V30/40 ,  G06V40/168 ,  G06V40/172 ,  G06V40/45

    Abstract: A method and system for establishing an association between a document and a person or verifying identity comprising capturing images of the person's face and processing the images to generate a face feature vector data. The face feature vector data can not be reverse processed to generate the image of the person's face. The method also captures images of the graphic on the item and processes those images to extract graphic derived feature vector data that is stored inside the graphic code. The graphic extracted face feature vector data cannot be reverse processed to generate the image of the person's face. Then, the face derived feature vector data is compared to the graphic derived feature vector data to develop a similarity value. Then the similarity value is compared to a first threshold value to develop a validation indicator representing the likelihood that the graphic/item are associated with the person.

    Predicting avatars for a user based on emotions of the user
    4.
    发明公开
    Predicting avatars for a user based on emotions of the user 审中-公开

    公开(公告)号：US20240338972A1

    公开(公告)日：2024-10-10

    申请号：US18296600

    申请日：2023-04-06

    Applicant: Bank of America Corporation

    Inventor： Gayathri Karthik ,  Kumaraguru Mohan ,  Vivek Jaiswal ,  Logesh MM ,  Vasantha Kumar Babu

    IPC: G06V40/16 ,  G06V10/75 ,  G06V10/774

    CPC classification number: G06V40/174 ,  G06V10/751 ,  G06V10/774 ,  G06V40/168

    Abstract: A system receives a first image and extracts a first set of facial features from the first image. The system determines that a user exhibited a first emotion in the first image. The system determines that the user selected a first avatar for use within a virtual environment. The system associates the first avatar with the first emotion of the user. The system receives a second image and extracts a second set of facial features from the second image. The system determines that the user exhibited a second emotion in the second image. The system determines that the first set of facial features corresponds to the second set of facial features. In response, the system presents the first avatar to the user. The system determines that the user selects the first avatar. The system implements the first avatar on behalf of the user for use in the virtual environment.

    SYSTEM AND METHOD FOR COMPLETING THREE DIMENSIONAL FACE RECONSTRUCTION
    6.
    发明公开
    SYSTEM AND METHOD FOR COMPLETING THREE DIMENSIONAL FACE RECONSTRUCTION 审中-公开

    公开(公告)号：US20240331279A1

    公开(公告)日：2024-10-03

    申请号：US18194793

    申请日：2023-04-03

    Applicant: Honda Motor Co., Ltd.

    Inventor： Kunal NANDANWAR ,  Nawid JAMALI ,  Hifza JAVED

    IPC: G06T17/00 ,  G06V40/16

    CPC classification number: G06T17/00 ,  G06V40/168

    Abstract: A system and method for completing three dimensional face reconstruction that includes receiving image data associated with multiple two dimensional non-frontal face images. The system and method also includes analyzing the image data and extracting two dimensional facial features. The system and method additionally includes constructing sparse three dimensional facial feature point clouds based on the two dimensional facial features. The system and method further includes inputting the sparse three dimensional facial feature point clouds into an encoder-decoder architecture to generate a three dimensional facial feature point cloud of complete facial features.

    System and method of mode selection face recognition with parallel CNNS
    9.
    发明授权
    System and method of mode selection face recognition with parallel CNNS 有权

    公开(公告)号：US12080101B2

    公开(公告)日：2024-09-03

    申请号：US17713636

    申请日：2022-04-05

    Applicant: GenkiTek Technology Co., Ltd.

    Inventor： Yun-Nien Huang ,  Chih-Tsung Shen ,  Hsin-Jung Cheng

    IPC: G06V40/16 ,  G06N3/045 ,  G06V10/82

    CPC classification number: G06V40/172 ,  G06N3/045 ,  G06V10/82 ,  G06V40/168

    Abstract: A system and method of mode selection face recognition with parallel CNNs are disclosed. The system includes a mode selection module, a parallel convolutional neural network module, a database module and an ID judgment module. The parallel convolutional neural network module includes several convolutional neural network units. The mode selection module can determine the facial characteristics of each facial image, and send each facial image to the convolutional nerve corresponding to its facial characteristics The results serve as a benchmark for the ID judgment module to compare and test other facial images. By using several convolutional neural network units for facial image training for different facial characteristics of the same person, the noise from other convolutional neural network units can be reduced and the similarity in the feature vector extraction stage can be improved. The resulting accuracy of face recognition will increase.

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