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    Login access control for secure/private data
    41.
    发明授权
    Login access control for secure/private data 有权

    公开(公告)号：US10289825B2

    公开(公告)日：2019-05-14

    申请号：US15637644

    申请日：2017-06-29

    Applicant: NEC Laboratories America, Inc. ,  NEC Hong Kong Limited

    Inventor： Manmohan Chandraker ,  Xiang Yu ,  Eric Lau ,  Elsa Wong

    IPC: G06K9/00 ,  G06F21/32 ,  G06N20/00 ,  G06N99/00 ,  G07C9/00 ,  H04L29/06 ,  G06F21/62 ,  G06K9/46 ,  G06K9/66

    Abstract: A login access control system is provided. The login access control system includes a camera configured to capture an input image of a subject purported to be a person and attempting to login to a system to access secure data. The login access control system further includes a memory storing a deep learning model configured to perform multi-task learning for a pair of tasks including a liveness detection task and a face recognition task. The login access control system also includes a processor configured to apply the deep learning model to the input image to recognize an identity of the subject in the input image regarding being authorized for access to the secure data and a liveness of the subject. The liveness detection task is configured to evaluate a plurality of different distractor modalities corresponding to different physical spoofing materials to prevent face spoofing for the face recognition task.

    Security system for facility access control
    42.
    发明授权
    Security system for facility access control 有权

    公开(公告)号：US10289824B2

    公开(公告)日：2019-05-14

    申请号：US15637465

    申请日：2017-06-29

    Applicant: NEC Laboratories America, Inc. ,  NEC Hong Kong Limited

    Inventor： Manmohan Chandraker ,  Xiang Yu ,  Eric Lau ,  Elsa Wong

    IPC: G06K9/00 ,  G06F21/32 ,  G06N20/00 ,  G06N99/00 ,  G07C9/00 ,  H04L29/06 ,  G06F21/62 ,  G06K9/46 ,  G06K9/66

    Abstract: A facility access control system and corresponding method are provided. The facility access control system includes a camera configured to capture an input image of a subject attempting to enter or exit a restricted facility. The facility access control system further includes a memory storing a deep learning model configured to perform multi-task learning for a pair of tasks including a liveness detection task and a face recognition task. The facility access control system also includes a processor configured to apply the deep learning model to the input image to recognize an identity of the subject in the input image regarding being authorized for access to the facility and a liveness of the subject. The liveness detection task is configured to evaluate a plurality of different distracter modalities corresponding to different physical spoofing materials to prevent face spoofing for the face recognition task.

    MOBILE DEVICE WITH ACTIVITY RECOGNITION
    43.
    发明申请
    MOBILE DEVICE WITH ACTIVITY RECOGNITION 审中-公开

    公开(公告)号：US20190138812A1

    公开(公告)日：2019-05-09

    申请号：US16112040

    申请日：2018-08-24

    Applicant: NEC Laboratories America, Inc.

    Inventor： Kihyuk Sohn ,  Manmohan Chandraker ,  Xiang Yu

    IPC: G06K9/00 ,  G06K9/62

    Abstract: A computer-implemented method, system, and computer program product are provided for activity recognition in a mobile device. The method includes receiving a plurality of unlabeled videos from one or more cameras. The method also includes generating a classified video for each of the plurality of unlabeled videos by classifying an activity in each of the plurality of unlabeled videos. The method additionally includes storing the classified video in a location in a memory designated for videos of the activity in each of the classified videos.

    LONG-TAIL LARGE SCALE FACE RECOGNITION BY NON-LINEAR FEATURE LEVEL DOMAIN ADAPTION
    44.
    发明申请
    LONG-TAIL LARGE SCALE FACE RECOGNITION BY NON-LINEAR FEATURE LEVEL DOMAIN ADAPTION 审中-公开

    公开(公告)号：US20190095705A1

    公开(公告)日：2019-03-28

    申请号：US16145537

    申请日：2018-09-28

    Applicant: NEC Laboratories America, Inc.

    Inventor： Xiang Yu ,  Xi Yin ,  Kihyuk Sohn ,  Manmohan Chandraker

    IPC: G06K9/00 ,  G06K9/62

    Abstract: A computer-implemented method, system, and computer program product are provided for facial recognition. The method includes receiving, by a processor device, a plurality of images. The method also includes extracting, by the processor device with a feature extractor utilizing a convolutional neural network (CNN) with an enlarged intra-class variance of long-tail classes, feature vectors for each of the plurality of images. The method additionally includes generating, by the processor device with a feature generator, discriminative feature vectors for each of the feature vectors. The method further includes classifying, by the processor device utilizing a fully connected classifier, an identity from the discriminative feature vector. The method also includes control an operation of a processor-based machine to react in accordance with the identity.

    LONG-TAIL LARGE SCALE FACE RECOGNITION BY NON-LINEAR FEATURE LEVEL DOMAIN ADAPTION
    45.
    发明申请
    LONG-TAIL LARGE SCALE FACE RECOGNITION BY NON-LINEAR FEATURE LEVEL DOMAIN ADAPTION 审中-公开

    公开(公告)号：US20190095700A1

    公开(公告)日：2019-03-28

    申请号：US16145608

    申请日：2018-09-28

    Applicant: NEC Laboratories America, Inc.

    Inventor： Xiang Yu ,  Xi Yin ,  Kihyuk Sohn ,  Manmohan Chandraker

    IPC: G06K9/00 ,  G06N3/08 ,  G06K9/62 ,  G06Q30/02 ,  G06Q20/40 ,  H04N7/18

    Abstract: A computer-implemented method, system, and computer program product are provided for facial recognition. The method includes receiving, by a processor device, a plurality of images. The method also includes extracting, by the processor device with a feature extractor utilizing a convolutional neural network (CNN) with an enlarged intra-class variance of long-tail classes, feature vectors for each of the plurality of images. The method additionally includes generating, by the processor device with a feature generator, discriminative feature vectors for each of the feature vectors. The method further includes classifying, by the processor device utilizing a fully connected classifier, an identity from the discriminative feature vector. The method also includes control an operation of a processor-based machine to react in accordance with the identity.

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