Recurrent neural network-based discrete emotion recognition method

A technology of recursive neural network and emotion recognition, applied in character and pattern recognition, instruments, computer parts, etc., to achieve the effect of precise recognition

Active Publication Date: 2016-04-06
北京中科欧科科技有限公司
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

However, this method ignores that emotion expression is a dynamic process, and the dynamic information in emotion expression plays an important role in emotion recognition.
Only through the method of maximum pooling feature sequence, the dynamic information of emotional expression is completely ignored, and there is a great loss of information

Method used

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  • Recurrent neural network-based discrete emotion recognition method
  • Recurrent neural network-based discrete emotion recognition method
  • Recurrent neural network-based discrete emotion recognition method

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Embodiment Construction

[0037] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0038] It should be noted that, in the drawings or descriptions of the specification, similar or identical parts all use the same figure numbers. The implementations shown or described in the accompanying drawings are forms known to those of ordinary skill in the art. It should be pointed out that the described examples are only considered for the purpose of illustration and not limitation of the present invention.

[0039] Such as figure 1 Shown, the discrete emotion recognition method based on recursive neural network of the present invention comprises the following steps:

[0040] Step 1, extracting facial expression features from the image signal in the video, such as figure 2 Shown:

[0041] Step 11, face detect...

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Abstract

The invention provides a recurrent neural network-based discrete emotion recognition method. The method comprises the following steps: 1, carrying out face detecting and tracking on image signals in a video, extracting key points of faces to serve as deformation features of the faces after obtaining the face regions, clipping the face regions and normalizing to a uniform size and extracting the appearance features of the faces; 2, windowing audio signals in the video, segmenting audio sequence units out and extracting audio features; 3, respectively carrying out sequential coding on the three features obtained by utilizing a recurrent neural network with long short-term memory models to obtain emotion representation vectors with fixed lengths, connecting the vectors in series and obtaining final emotion expression features; and 4, carrying out emotion category prediction by utilizing the final emotion expression features obtained in the step 3 on the basis of a support vector machine classifier. According to the method, dynamic information in the emotion expressing process can be fully utilized, so that the precise recognition of emotions of participators in the video is realized.

Description

technical field [0001] The invention belongs to the field of video signal processing, and in particular relates to a discrete emotion recognition method based on a recursive neural network with a long-short-term memory model, thereby improving the accuracy of the discrete emotion recognition. Background technique [0002] In recent years, researchers at home and abroad have done a lot of research work on discrete emotion recognition, and proposed many effective algorithms for emotion recognition. These methods can be divided into emotion recognition based on static images and emotion recognition based on dynamic videos from the processing strategy. The main framework of video-based emotion recognition work is: first extract the features of each frame image in the video, then maximize these features and obtain emotional features of uniform length, and finally use this feature for emotional classification. The framework uniformly maps feature sequences of different lengths in...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/176G06F18/2411G06F18/25G06F18/214
Inventor 陶建华巢林林杨明浩李雅温正棋
Owner 北京中科欧科科技有限公司
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