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A facial expression recognition method based on multi-task convolutional neural network

A technology of convolutional neural network and facial expression recognition, which is applied in the field of computer vision, can solve the problem of expanding the difference between feature classes, and achieve the effect of optimizing results and high discrimination

Active Publication Date: 2021-10-19
XIAMEN UNIV
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Problems solved by technology

The intra-class loss can effectively reduce the intra-class difference of features, however, the intra-class loss does not explicitly expand the inter-class difference of features

Method used

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  • A facial expression recognition method based on multi-task convolutional neural network
  • A facial expression recognition method based on multi-task convolutional neural network
  • A facial expression recognition method based on multi-task convolutional neural network

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Embodiment Construction

[0049] The method of the present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0050] see figure 1 , the implementation of the embodiment of the present invention includes the following steps:

[0051] 1. Design a multi-task convolutional neural network. For the input image, the first part of the network is used to extract the low-level semantic features of the image, and based on the extracted low-level semantic features, multiple parallel fully connected layers are used to further extract the high-level semantic features of the network.

[0052] 2. In the designed multi-task convolutional neural network, multi-task learning is used to simultaneously perform multiple single-expression discriminative feature learning tasks and multi-expression recognition tasks, and use a joint loss to supervise each single-expression discrimination task , for learning discriminative features for a certain expression.

[0053] B...

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Abstract

A facial expression recognition method based on multi-task convolutional neural network. Firstly, a multi-task convolutional neural network structure is designed, and the low-level semantic features shared by all expressions and multiple single-expression discriminative features are sequentially extracted in the network; Task learning, learn multiple single-expression discriminative feature learning tasks and multi-expression recognition tasks at the same time, use a joint loss to supervise all tasks of the network, and use two loss weights to balance the loss of the network; finally according to the trained network The final facial expression recognition result is obtained from the final flexible maximum classification layer of the model. Put feature extraction and expression classification in an end-to-end framework for learning, extract discriminative features from input images, and make reliable expression recognition for input images. Through experimental analysis, it can be seen that the algorithm has excellent performance and can effectively distinguish complex facial expressions, and has achieved good recognition performance on multiple public data sets.

Description

technical field [0001] The invention relates to computer vision technology, in particular to a method for recognizing facial expressions based on a multi-task convolutional neural network. Background technique [0002] In the past few decades, automatic facial expression recognition has attracted more and more computer vision experts and scholars. The goal of facial expression recognition is to design a system for a given facial expression picture that can automatically predict the facial expression category it belongs to. Facial expression automatic recognition technology has a wide range of application scenarios, such as human-computer interaction, safe driving and healthcare. Despite the success of this technology over the years, reliable automatic facial expression recognition under uncontrolled environmental conditions remains a great challenge. [0003] A facial expression recognition system consists of three modules: face detection, feature extraction and facial exp...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06N3/04
CPCG06V40/176G06N3/045
Inventor 严严黄颖王菡子
Owner XIAMEN UNIV
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