A facial expression recognition method based on MB-2DPCA features

Abstract

The invention discloses a facial expression recognition method based on MB-2DPCA features. The method comprises the following steps: carrying out local texture feature extraction on a facial expression image through a multi-scale parameter MB-LBP operator; carrying out feature extraction on expression features and constructing an MB-LBP feature space of the image; calculating a projection matrix W through a 2DPCA algorithm in training samples, and mapping all of the training samples to a subspace through the projection matrix W, and obtaining feature expression of the expression image in the subspace; and mapping a test sample to the subspace through the projection matrix W, and carrying out classification on the features of the test sample through a sparse representation classifier to obtain the category of the test sample. The method prevents the problem of large covariance matrix calculation dimensions due to conversion from an image matrix to the image vector, and the problem of small samples due to fewer samples; and the calculation time is less.

Description

technical field [0001] The invention relates to image processing technology, in particular to a facial expression recognition method based on the combination of local and global features. Background technique [0002] Emoticons are a non-verbal way for humans to express their inner emotions. People can not only accurately express their true inner feelings through expressions, but also understand each other's emotions and thoughts by judging the changes in expressions. This non-verbal communication method has been widely used in interpersonal communication. In real life, facial expression analysis technology is used in many fields, including distance education, driver fatigue driving, remote patient care, pain assessment, human-computer interaction in video games, etc. Because of the importance and potential application value of expression analysis, in the research field of computer vision and machine learning, facial expression recognition has always been the focus of resea...

AI Technical Summary

Problems solved by technology

The method of subspace projection usually converts the image matrix into an image vector to generate high-dimensional data. The dimension of the pattern feature subspace far exceeds the number of various training samples, resulting in the problem of high-dimensional small samples

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