Facial expression recognition method based on inter-class difference strengthening network

A facial expression recognition and network technology, applied in the field of facial expression recognition, can solve the problem of ignoring high similarity, achieve good classification effect, improve classification effect, and reduce impact.

Pending Publication Date: 2022-03-25
SOUTHEAST UNIV
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  • Abstract
  • Description
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  • Application Information

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Problems solved by technology

However, these methods ignore the characteristic of hi

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  • Facial expression recognition method based on inter-class difference strengthening network
  • Facial expression recognition method based on inter-class difference strengthening network
  • Facial expression recognition method based on inter-class difference strengthening network

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Embodiment example

[0076] This implementation case uses Python3.7 and Pytorch deep learning framework as the experimental platform, and uses a GeForce RTX 3070 graphics card with 8G memory as the training tool. For the FER2013 dataset, use Training as the training set (the number of samples is 28709), PrivateTest as the test set (the number of samples is 3589), and PublicTest as the verification set (the number of samples is 3589). For the RAF-DB dataset, the initial division of the original data into the training set (the number of samples is 12271) and the test set (the number of samples is 3068) is used as the basis for the division of this example. This implementation case does not use any dataset to pre-train the model. The training process of the two data sets uses the same hyperparameter settings: the maximum number of training iterations is 150; the batch_size is 48; the RAdam optimizer is used; the plateau_patience is set to 2; the initial learning rate is 0.01; weight_decay is 0.0001. ...

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Abstract

The invention discloses a facial expression recognition method based on an inter-class difference strengthening network, and the method comprises the following steps: collecting a data set, and carrying out the preprocessing of the data set; analyzing an expression similarity relationship; constructing a parallel branch network by utilizing similarity information, and respectively extracting global features and fine-grained features similar to distinguishing similar expressions; the extracted features are respectively sent to a full connection layer for dimension reduction, feature fusion is carried out, and expression categories are output through a classifier; and adding a class balance weighted loss function so as to enlarge the class spacing. According to the method, expression similarity information is fully utilized, fine-grained features are extracted, and the face expression classification effect is effectively improved.

Description

technical field [0001] The invention belongs to the technical field of facial expression recognition, and in particular relates to a method for recognizing facial expressions based on a dual-branch attention mechanism. Background technique [0002] Expressions contain rich human emotional information and are one of the main ways for humans to communicate with each other. Facial expression recognition aims to mine potential emotional features from facial images and classify them. It is a research hotspot in the field of computer vision and has shown extensive applications in many fields such as autonomous driving, classroom teaching, clinical psychology, and intelligent transportation. application prospects. [0003] At present, the research on facial expression recognition based on deep learning has made great progress. Researchers have proposed various effective algorithms to improve the feature learning ability of the network, including: extracting auxiliary input signals...

Claims

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Application Information

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IPC IPC(8): G06V40/16G06V10/80G06V10/82G06V10/764G06V10/74G06V10/774G06N3/04G06N3/08G06K9/62
CPCG06N3/08G06N3/045G06F18/22G06F18/253G06F18/24G06F18/214
Inventor 达飞鹏蒋倩
Owner SOUTHEAST UNIV
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