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
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[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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