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Expression recognition method based on packet convolutional neural network

A convolutional neural network and facial expression recognition technology, applied in the field of facial expression recognition based on grouped convolutional neural networks, can solve the problems of deepening the model, excessive model parameters, unfavorable model training and practical application, etc. Accurate, high real-time effects

Inactive Publication Date: 2020-05-08
BEIJING UNIV OF CIVIL ENG & ARCHITECTURE
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Problems solved by technology

However, in order to obtain higher accuracy, scholars continue to deepen the depth of the model, resulting in too many model parameters
This is not conducive to the training of the model and the use of practical applications

Method used

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  • Expression recognition method based on packet convolutional neural network
  • Expression recognition method based on packet convolutional neural network
  • Expression recognition method based on packet convolutional neural network

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Embodiment 1

[0046] This embodiment provides a method for facial expression recognition based on grouped convolutional neural networks, such as Figure 1-4 shown. like figure 1 As shown, an expression recognition method based on a grouped convolutional neural network provided in this embodiment includes four parts: inputting a single frame image, face detection, face correction, and expression recognition. Starting from the original input image, after two stages of image processing, it then predicts the classification of facial expressions.

[0047] The specific construction process of the face detector provided in this embodiment is as follows:

[0048] First of all, recognition accuracy and calculation time are the two criteria for detecting and locating faces in the human-computer interaction environment, but considering the real-time nature of the expression recognition system, under the premise of ensuring a certain accuracy, it is necessary to select features with faster calculati...

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Abstract

The invention relates to the field of artificial intelligence, and specifically provides an expression recognition method based on a packet convolutional neural network. The method comprises the following steps: S1, building and training a packet convolutional neural network model; S2, extracting input image information, decomposing each convolution layer into deep convolution and point convolution by the packet convolutional network model, and grouping each point convolution; S3, building a face corrector; S4, detecting and correcting the input image by adopting the face corrector to obtain apreprocessed image; and S5, classifying and preprocessing the facial expressions in the image by adopting the packet convolutional neural network model. The technical problems of low recognition accuracy and low recognition speed in the prior art are solved, and high real-time performance is achieved while the accuracy is ensured.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to an expression recognition method based on a grouped convolutional neural network. Background technique [0002] Emotion is a cognitive experience produced by human beings under strong psychological activities, and it is an important element that guides communication in social environments. Emotions can be triggered by a variety of sources, including mood, personality, motivation, etc. As a unique signal transmission system, facial expression can express people's psychological state and is one of the effective methods for analyzing emotions. Expression recognition mainly has the following four processes: face positioning, face correction, feature extraction and expression classification. Feature extraction and expression classification, as an important part of the process, are the core difficulties of expression recognition. Traditional methods use hand-designed geometric feature...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06V40/174G06N3/045
Inventor 赵光哲张雷杨瀚霆何艳清张倚萌
Owner BEIJING UNIV OF CIVIL ENG & ARCHITECTURE
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