Feature representation method based on dynamic facial expression sequence and K-order emotional intensity model

Abstract

The invention discloses a feature representation method based on a dynamic facial expression sequence and a K-order emotional intensity model. The method is characterized in that the method comprises the steps of (1) extracting a Haar feature in a dynamic facial expression sequence set and carrying out feature dimensionality reduction by using PCA, (2) taking each column of a feature matrix which is subjected to dimensionality reduction as a clustering sample and carrying out K-Means clustering, obtaining the mean and variance of all features in each class, constructing the normal distribution of each class, and generating a K-order emotional intensity model, (3) extracting the Haar feature in a dynamic facial expression sequence to be extracted and then carrying out feature dimensionality reduction by using the PCA, (4) inputting the feature which is subjected to the dimensionality reduction into the K-order emotional intensity model and obtaining an output matrix, and (5) encoding the output matrix to obtain an encoding result which is the feature representation of dynamic facial expression sequence to be extracted. According to the method, the feature dimensionality and computation complexity can be effectively reduced, and the real-time performance of feature extraction is improved.

Description

technical field [0001] The invention relates to a feature representation method, which belongs to the field of image processing, in particular to a feature representation method based on a dynamic facial expression sequence and a K-order emotional intensity model. Background technique [0002] Facial expression recognition is an important part of emotional computing and advanced intelligence, and it is also a research hotspot in the fields of human-computer interaction, machine learning, intelligent control and image processing. In order to promote more natural and human-like human-computer interaction, in-depth research on expression recognition becomes more important. Among them, the research on dynamic facial expression sequence has attracted more attention, because human facial expression is a complete process with a beginning, a climax and an end, so facial expression recognition for dynamic sequence images can more effectively reflect the essence of the human facial ex...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the expression images need to be strictly aligned, otherwise the change of expression cannot be truly reflected.

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