A facial expression recognition method based on convolution neural network

A convolutional neural network and facial expression recognition technology, applied in the field of facial expression recognition based on convolutional neural network, can solve the problems of low facial expression recognition efficiency, inaccurate positioning, complicated process, etc., to improve the recognition efficiency , the effect of changing the accuracy and reducing the complexity

Active Publication Date: 2018-12-18
LIAONING TECHNICAL UNIVERSITY
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Problems solved by technology

However, due to the inaccurate positioning of the extracted feature points and the lack of effective featur

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  • A facial expression recognition method based on convolution neural network
  • A facial expression recognition method based on convolution neural network
  • A facial expression recognition method based on convolution neural network

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[0045] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0046] Such as figure 1 As shown, the method of this embodiment is as follows.

[0047] Step 1, collect facial expression pictures through digital cameras, mobile phones or monitoring equipment, use the Internet to download FER-2013 face database and CK+ face database, obtain larger images about people's faces in order of magnitude, and divide the images into There are two parts of training set and test set.

[0048] Step 2. Preprocess the collected images, cut the collected images to a size of 96*96 pixels, place the face in the center of the image, and use matlab software to grayscale the color image in the face database After processing, a grayscale image of 96*96 si...

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Abstract

The invention provides a face expression recognition method based on a convolution neural network, which relates to the technical field of face expression recognition. At first, the method collects facial expression pictures and downloads the FER-2013 face database and CK + face database; the images are divided into a training set and a test set, then the collected images are preprocessed to obtain the 96*96 size gray-scale images, and the convolution neural network model is established; the training set is used for training and the error between the actual output result of training and the label value is calculated, and the difference value is transmitted from top to bottom through the back propagation algorithm, and the weight value is updated by the weight value update formula. After the training, the network model is saved, the image of the test set is inputted into the training model, and the recognition rate is calculated. In the invention, the face expression recognition methodis improved, the convergence speed of the model is improved, the recognition efficiency is improved, the accuracy rate of the convolution neural network is changed, and the face expression recognitionefficiency is improved to a certain extent.

Description

technical field [0001] The invention relates to the technical field of facial expression recognition, in particular to a method for recognizing facial expressions based on a convolutional neural network. Background technique [0002] Facial expressions are an effective way to convey emotion. Expressions contain a lot of effective information about emotions; as a technology that can automatically identify faces, expression recognition has a high recognition efficiency for a single face image; due to the differences in the expressions of different people, the recognition rate is reduced . The expression recognition process is to reduce the existing differences through feature point extraction. However, due to the inaccurate positioning of the extracted feature points and the lack of effective feature points, the efficiency of facial expression recognition is low, and the process is relatively complicated. Facial expression recognition can be applied in many fields such as m...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V40/171G06V40/174G06V10/467G06N3/048G06N3/045G06F18/24
Inventor 姜彦吉葛少成郭羽含王光杨帆
Owner LIAONING TECHNICAL UNIVERSITY
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