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Facial emotion recognition method based on local sparse representation classifier

A local sparse and facial emotion technology, applied in the field of facial emotion recognition, can solve the problems of data measurement failure and lack of basis for classification

Inactive Publication Date: 2015-05-20
GUANGZHOU HUAJIU INFORMATION TECH
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  • Application Information

AI Technical Summary

Problems solved by technology

Sparse leads to failure of measurement between data, which in turn leads to lack of basis for classification

Method used

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  • Facial emotion recognition method based on local sparse representation classifier
  • Facial emotion recognition method based on local sparse representation classifier
  • Facial emotion recognition method based on local sparse representation classifier

Examples

Experimental program
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Embodiment Construction

[0070] like figure 1 As shown, it is a face emotion recognition method based on a local sparse representation classifier, which includes the following steps:

[0071] [1] Collect facial expression images, and save the collected facial expression images as JPEG format image files with the time of collection as the file name;

[0072] [2] Using Gabor wavelet transform to construct the feature vector of facial expression;

[0073] [3] Use the MCFS algorithm to select features and obtain the feature vectors of facial expressions after dimensionality reduction;

[0074] [4] Use a local relatively sparse classifier to classify the emotion categories represented by the dimensionality-reduced feature vectors, and the judged emotion categories are anger, happiness, sadness, surprise, disgust, fear and calm.

[0075] (1) Face collection and detection method

[0076] In this implementation case, the face acquisition and detection uses the API functions provided by OpenCV. OpenCV is...

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Abstract

The invention discloses a facial emotion recognition method based on a local sparse representation classifier. The method is characterized by including: collecting facial emotion images; using Gabor wavelet transformation to construct the feature vectors of the facial emotion images; using a feature selection algorithm MFCS to select features; using the local sparse representation classifier to recognize emotion classification. Compared with the prior art, the method is high in emotion classification recognition accuracy, fast in emotion recognition, insensitive to faces, and the like.

Description

technical field [0001] The invention provides a face emotion recognition method based on a local sparse representation classifier, which belongs to the technical fields of medical health, image processing and pattern recognition. Background technique [0002] With the continuous development of information technology, affective computing has been widely used in intelligent robots, intelligent toys, games, e-commerce and other fields to build a more anthropomorphic style and more realistic scenes. For example, in terms of human-computer interaction, a computer with emotional capabilities can acquire, classify, recognize, and respond to human emotions, thereby helping users to obtain an efficient and friendly feeling, and can effectively reduce people's frustration in using computers, and even Can help people understand their own and other people's emotional world. In terms of intelligent transportation, emotional computing technology can be used to detect whether the driver's...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/174G06V40/171G06F18/24G06F18/214
Inventor 不公告发明人
Owner GUANGZHOU HUAJIU INFORMATION TECH
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