公开(公告)号：US20210390346A1

公开(公告)日：2021-12-16

申请号：US17354898

申请日：2021-06-22

Applicant: Beijing Baidu Netcom Science and Technology Co., Ltd.

Inventor： Fei Tian

IPC: G06K9/62 ,  G06K9/00

Abstract: Embodiments of the present disclosure disclose a method and apparatus for training a cross-modal face recognition model, a device and a storage medium. The method may include: acquiring a first modal face recognition model having a predetermined recognition precision; acquiring a first modality image of a face and a second modality image of the face; acquiring a feature value of the first modality image of the face and a feature value of the second modality image of the face; and constructing a loss function based on a difference between the feature value of the first modality image of the face and the feature value of the second modality image of the face, and tuning a parameter of the first modal face recognition model based on the loss function until the loss function converges, to obtain a trained cross-modal face recognition model.

公开(公告)号：US11854118B2

公开(公告)日：2023-12-26

申请号：US17152293

申请日：2021-01-19

Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

Inventor： Fei Tian

IPC: G06T11/00 ,  G06N3/088 ,  G06T7/00 ,  G06N3/045 ,  G06V10/764 ,  G06V10/80 ,  G06V10/82 ,  G06V10/143 ,  G06V40/16

CPC classification number: G06T11/001 ,  G06N3/045 ,  G06N3/088 ,  G06T7/0014 ,  G06V10/143 ,  G06V10/764 ,  G06V10/803 ,  G06V10/82 ,  G06V40/171 ,  G06T2207/10048 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30201

Abstract: A method for training generative network, a method for generating near-infrared image and device. The method includes: obtaining a training sample set, in which the set includes near-infrared image samples and visible-light image samples; obtaining an adversarial network to be trained, in which the generative network of the adversarial network is configured to generate each near-infrared image according to an input visible-light image, the discrimination network of the adversarial network is configured to determine whether an input image is real or generated; constructing a first objective function according to a first distance between each generated near-infrared image and the corresponding near-infrared image sample in an image space and a second distance between each generated near-infrared image and the corresponding near-infrared image sample in a feature space; performing an adversarial training on the adversarial network with the set based on optimizing a value of the first objective function.