Face expression identification method based on parallel convolutional neural network characteristic graph fusion

A technology of convolutional neural network and facial expression recognition, which is applied in the field of image recognition, can solve the problem of unsatisfactory recognition of micro-expression features, and achieve the effect of increasing feature expression ability and facilitating training

Active Publication Date: 2018-10-02
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

The conventional CNN, DBN, and RNN models have a certain recognition effect on expressions w

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  • Face expression identification method based on parallel convolutional neural network characteristic graph fusion
  • Face expression identification method based on parallel convolutional neural network characteristic graph fusion
  • Face expression identification method based on parallel convolutional neural network characteristic graph fusion

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[0043] The technical solutions in the embodiments of the present invention will be described clearly and in detail below with reference to the drawings in the embodiments of the present invention. The described embodiments are only some of the embodiments of the invention.

[0044] The technical scheme that the present invention solves the problems of the technologies described above is:

[0045] The feature fusion facial expression recognition method of a kind of parallel convolutional neural network provided by the present embodiment comprises the following steps:

[0046] (1) The acquired facial expression image is preprocessed by intercepting the face area and numerical normalization:

[0047] Capture face area: Get the face area and select an image area with a size of 256×256 for interception.

[0048] Normalization: Normalize the acquired image so that the image value is in the range of [0,1]. Divide the image numerical matrix by 255 to obtain the image matrix informa...

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Abstract

The invention provides a face expression identification method based on parallel convolutional neural network characteristic graph fusion. The method comprises the steps of designing a convolutional neural network with a parallel structure through simulating vision channels of double eyes of a man, fusing the characteristic graph of the parallel channels after a convolutional pooled layer; on a full connecting layer structure, using sparse full-connection output to one channel, and using dense full-connection output to the other channel, and finally fusing the two outputs and performing classification. After the face expression data are used for performing model training and a relatively high identification rate is realized, a model identification effect is tested by means of a testing sample, and furthermore a relatively high identification accuracy is acquired. The face expression identification method is a new method for emotion analysis and face expression identification.

Description

technical field [0001] The invention belongs to the field of image recognition, in particular to a method for extracting and recognizing facial expression features by using a parallel convolutional neural network. Background technique [0002] Facial expressions contain rich emotional real information, and accurate and efficient recognition of facial expressions is an important research direction in the field of image vision. Facial expression information can be used in many fields such as distance education, auxiliary medical treatment, criminal detection and lie detection. Facial expression recognition technology is a process of classifying and identifying feature information after extracting facial expression features through a specific method. [0003] At present, the commonly used feature extraction methods for facial expression recognition can be divided into methods based on shape models and texture models. Among them, the active appearance model is mainly based on ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V40/174G06N3/045G06F18/2411G06F18/253
Inventor 蔡军昌泉蔡芳唐贤伦陈晓雷魏畅伍亚明林文星
Owner CHONGQING UNIV OF POSTS & TELECOMM
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