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Face emotion recognition method based on dual-stream convolutional neural network

机译:基于双流卷积神经网络的面部情感识别方法

摘要

A face emotion recognition method based on dual-stream convolutional neural network uses a multi-scale face expression recognition network to single frame face images and face sequences to perform learning classification. The method includes constructing a multi-scale face expression recognition network which includes a channel network with a resolution of 224×224 and a channel network with a resolution of 336×336, extracting facial expression characteristics at different resolutions through the recognition network, effectively combining static characteristics of images and dynamic characteristics of expression sequence to perform training and learning, fusing the two channel models, testing and obtaining a classification effect of facial expressions. The present invention fully utilizes the advantages of deep learning, effectively avoids the problems of manual extraction of feature deviations and long time, and makes the method provided by the present invention more adaptable. Moreover, the present invention improves the accuracy and productivity of expression recognition.
机译:基于双流卷积神经网络的面部情感识别方法使用多尺度面部表达式识别网络到单帧面部图像和面部序列来执行学习分类。该方法包括构建多尺度面部表达识别网络,其包括分辨率为224×224的频道网络和具有336×336的分辨率的信道网络,通过识别网络提取不同分辨率的面部表情特性,有效地组合表达序列的图像和动态特征的静态特征,执行训练和学习,融合两个信道模型,测试和获得面部表情的分类效果。本发明充分利用深度学习的优点,有效地避免了手动提取特征偏差的问题,并且使得本发明提供的方法更适应。此外,本发明提高了表达式识别的准确性和生产率。

著录项

  • 公开/公告号US11010600B2

    专利类型

  • 公开/公告日2021-05-18

    原文格式PDF

  • 申请/专利权人 SICHUAN UNIVERSITY;

    申请/专利号US201916449458

  • 申请日2019-06-24

  • 分类号G06K9;G06K9/62;G06N3/08;G06N3/04;

  • 国家 US

  • 入库时间 2022-08-24 18:43:15

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