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METHOD AND APPARATUS FOR TRAINING CROSS-MODAL FACE RECOGNITION MODEL, DEVICE AND STORAGE MEDIUM

机译:用于训练跨模型面部识别模型,设备和存储介质的方法和装置

摘要

Embodiments of the present disclosure disclose a method and apparatus for training a cross-modal face recognition model, a device and a storage medium, and relates to the fields of computer vision, deep learning, big data and face recognition technologies. A specific implementation of the method includes: 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. Therefore, a domain migration technology is adopted to migrate the classification capability of the first modal recognition model of the face to other modal data of the face, so as to achieve the purpose of enhancing the capability to recognize other modalities of the face.
机译:本公开的实施例公开了一种用于训练跨模型面部识别模型,设备和存储介质的方法和装置,并且涉及计算机视觉,深度学习,大数据和面部识别技术的领域。该方法的特定实现包括:获取具有预定识别精度的第一模态面识别模型;获取面部的第一模态图像和面部的第二模态图像;获取面部的第一模态图像的特征值和面部的第二模态图像的特征值;基于面部的第一模态图像的特征值与面部的第一个模态图像的特征值与面部的特征值构成损失功能,并基于损耗函数调谐第一模态面部识别模型的参数直到损耗函数收敛,以获得培训的跨模型面部识别模型。因此,采用域迁移技术将面对面的第一模态识别模型的分类能力迁移到面部的其他模态数据,以达到增强识别面部其他方式的能力的目的。

著录项

  • 公开/公告号EP3893153A3

    专利类型

  • 公开/公告日2021-12-29

    原文格式PDF

  • 申请/专利号EP20210180579

  • 发明设计人 TIAN FEI;

    申请日2021-06-21

  • 分类号G06K9;G06K9/62;

  • 国家 EP

  • 入库时间 2022-08-24 23:06:13

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