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    Deep face recognition based on clustering over unlabeled face data
    1.
    发明授权
    Deep face recognition based on clustering over unlabeled face data 有权

    公开(公告)号：US11600113B2

    公开(公告)日：2023-03-07

    申请号：US17091066

    申请日：2020-11-06

    Applicant: NEC Laboratories America, Inc.

    Inventor： Xiang Yu ,  Manmohan Chandraker ,  Kihyuk Sohn ,  Aruni RoyChowdhury

    IPC: G06V40/16 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08

    Abstract: A computer-implemented method for implementing face recognition includes obtaining a face recognition model trained on labeled face data, separating, using a mixture of probability distributions, a plurality of unlabeled faces corresponding to unlabeled face data into a set of one or more overlapping unlabeled faces that include overlapping identities to those in the labeled face data and a set of one or more disjoint unlabeled faces that include disjoint identities to those in the labeled face data, clustering the one or more disjoint unlabeled faces using a graph convolutional network to generate one or more cluster assignments, generating a clustering uncertainty associated with the one or more cluster assignments, and retraining the face recognition model on the labeled face data and the unlabeled face data to improve face recognition performance by incorporating the clustering uncertainty.

    Universal feature representation learning for face recognition
    2.
    发明授权
    Universal feature representation learning for face recognition 有权

    公开(公告)号：US11580780B2

    公开(公告)日：2023-02-14

    申请号：US17091011

    申请日：2020-11-06

    Applicant: NEC Laboratories America, Inc.

    Inventor： Xiang Yu ,  Manmohan Chandraker ,  Kihyuk Sohn ,  Yichun Shi

    IPC: G06N20/00 ,  G06K9/62 ,  G06V40/16

    Abstract: A computer-implemented method for implementing face recognition includes receiving training data including a plurality of augmented images each corresponding to a respective one of a plurality of input images augmented by one of a plurality of variations, splitting a feature embedding generated from the training data into a plurality of sub-embeddings each associated with one of the plurality of variations, associating each of the plurality of sub-embeddings with respective ones of a plurality of confidence values, and applying a plurality of losses including a confidence-aware identification loss and a variation-decorrelation loss to the plurality of sub-embeddings and the plurality of confidence values to improve face recognition performance by learning the plurality of sub-embeddings.

    Siamese reconstruction convolutional neural network for pose-invariant face recognition
    5.
    发明授权
    Siamese reconstruction convolutional neural network for pose-invariant face recognition 有权

    公开(公告)号：US10474883B2

    公开(公告)日：2019-11-12

    申请号：US15803292

    申请日：2017-11-03

    Applicant: NEC Laboratories America, Inc.

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

    IPC: G06K9/00 ,  G06N3/04 ,  G06N3/08 ,  G08B13/196 ,  G06K9/62

    Abstract: A computer-implemented method, system, and computer program product is provided for pose-invariant facial recognition. The method includes generating, by a processor using a recognition neural network, a rich feature embedding for identity information and non-identity information for each of one or more images. The method also includes generating, by the processor using a Siamese reconstruction network, one or more pose-invariant features by employing the rich feature embedding for identity information and non-identity information. The method additionally includes identifying, by the processor, a user by employing the one or more pose-invariant features. The method further includes controlling an operation of a processor-based machine to change a state of the processor-based machine, responsive to the identified user in the one or more images.

    LONG-TAIL LARGE SCALE FACE RECOGNITION BY NON-LINEAR FEATURE LEVEL DOMAIN ADAPTION
    7.
    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：US20190095699A1

    公开(公告)日：2019-03-28

    申请号：US16145578

    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 ,  G08B13/196 ,  G08B15/00 ,  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.

    Domain adaptation for structured output via disentangled representations
    10.
    发明授权
    Domain adaptation for structured output via disentangled representations 有权

    公开(公告)号：US11604943B2

    公开(公告)日：2023-03-14

    申请号：US16400376

    申请日：2019-05-01

    Applicant: NEC Laboratories America, Inc.

    Inventor： Yi-Hsuan Tsai ,  Samuel Schulter ,  Kihyuk Sohn ,  Manmohan Chandraker

    IPC: G06K9/62 ,  G06N3/08

    Abstract: Systems and methods for domain adaptation for structured output via disentangled representations are provided. The system receives a ground truth of a source domain. The ground truth is used in a task loss function for a first convolutional neural network that predicts at least one output based on inputs from the source domain and a target domain. The system clusters the ground truth of the source domain into a predetermined number of clusters, and predicts, via a second convolutional neural network, a structure of label patches. The structure includes an assignment of each of the at least one output of the first convolutional neural network to the predetermined number of clusters. A cluster loss is computed for the predicted structure of label patches, and an adversarial loss function is applied to the predicted structure of label patches to align the source domain and the target domain on a structural level.

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