Tiny facial expression recognition method based on E-FCNN

A facial expression and recognition method technology, applied in the field of image recognition, can solve problems such as low definition and low feature accuracy, and achieve the effect of solving over-fitting problems and enhancing accuracy

Active Publication Date: 2020-06-23
SHANGHAI UNIVERSITY OF ELECTRIC POWER
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Problems solved by technology

The feature accuracy extracted by the feature extraction part also puts forward certain requirements on the input image. If the definition of the input image is not high or the input image is small, the extracted feature accuracy will also be reduced.

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  • Tiny facial expression recognition method based on E-FCNN
  • Tiny facial expression recognition method based on E-FCNN
  • Tiny facial expression recognition method based on E-FCNN

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specific Embodiment

[0037] 1. E-FCNN network framework

[0038] The basic structure of the E-FCNN network model designed by the present invention is as figure 1 As shown, based on the super-resolution network and expression recognition network, the super-resolution network is used to preprocess the input image. The purpose is to increase the resolution of the input image, thereby improving the accuracy of feature extraction and classification accuracy of the recognition part.

[0039] It can be seen that the super-resolution part is divided into three network modules, namely the edge enhancement network module, the upsampling network module and the feedback-based super-resolution network module. The edge enhancement network module extracts and enhances the edges of the input face. Since the edge part plays a decisive role in the feature extraction of the face, the extraction and enhancement of the face edge can effectively enhance the accuracy of recognition. The upsampling network module perfo...

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Abstract

The invention relates to a tiny facial expression recognition method based on an E-FCNN. The method comprises the following steps that 1, a marginalized face enhancement module, an up-sampling networkmodule and a super-resolution network module based on feedback are established respectively, and the marginalized face enhancement module is used for carrying out edge extraction and enhancement on an input face; wherein the up-sampling network module is used for carrying out interpolation method up-sampling on an input image, and the super-resolution network module based on feedback is used forcarrying out super-resolution processing on the input image; 2, establishing an E-FCNN network model in combination with an marginalized face enhancement module, an up-sampling network module and a super-resolution network module based on feedback; and 3, inputting the facial expression image data of the specific size into the E-FCNN network model to obtain a corresponding facial expression recognition result. Compared with the prior art, the method has the advantages of high recognition precision, high recognition speed and the like.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to an E-FCNN-based micro facial expression recognition method. Background technique [0002] Facial expression recognition has a wide range of applications in human-computer interaction, communication, robot manufacturing, transportation, facial nerve analysis, clinical psychology and other fields. With the development of the field of artificial intelligence, intelligence has gradually penetrated into all aspects of people's lives, and people's emotions can be obtained through the recognition of facial expressions, so that artificial intelligence robots are no longer cold machines, but as a machine that can read human emotions "Smart people" come to better serve mankind. [0003] In the past few decades, most expression recognition programs have tended to use larger images for recognition, and the input images are relatively clear. However, in our daily life, facial imag...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06T3/40G06T7/13
CPCG06T7/13G06T3/4053G06V40/174
Inventor 邵洁程其玉
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
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