A double-dictionary and multi-feature fusion decision-making face expression recognition method based on sparse representation

Abstract

The invention discloses a double-dictionary and multi-feature fusion decision facial expression recognition method based on sparse representation, and the method comprises the steps of firstly, extracting features from an expression-free face image sample and a specific expression face image sample, and constructing a nominal dictionary and a feature dictionary according to the features; Image tobe identified, extracting the corresponding features, performing sparse coding on the features by adopting a nominal dictionary, combining a coding coefficient result with the nominal dictionary to obtain reconstructed expression-free image features, subtracting the features before and after reconstruction to obtain the features only containing expression feature information, and performing sparsecoding on the features by adopting the feature dictionary to obtain a coding coefficient vector; training auxiliary decision fusion dictionaries for different types of features on the basis of the feature dictionaries, and performing classification judgment on the coding coefficient vectors calculated from the different types of features on the basis of sparse representation to obtain judgment results of the various types of features; obtaining a final identification result in a voting mode. The method can effectively overcome the influence of face, illumination, shielding and other changes on expression recognition.

Description

technical field [0001] The invention belongs to the technical field of facial expression recognition, and more specifically relates to a method for recognizing facial expressions based on a sparse representation-based double dictionary and multi-feature fusion decision-making. Background technique [0002] Facial expression recognition technology has strong application prospects in human-computer interaction, online education, intelligent driving and other fields. In actual use, facial expression recognition is easily affected by illumination, noise, and occlusion. In order to overcome the influence of these factors, the recognition algorithm framework based on sparse representation theory has been widely used. [0003] At present, the most important feature that affects the accuracy of facial expression recognition based on sparse representation theory is intra-class variation. For facial expression recognition, the feature differences between different types of expressions...

AI Technical Summary

Problems solved by technology

[0004] Lee Seung Ho et al. published "Intra-Class Variation Reduction Using Training Expression Images for Sparse Representation Based Facial Expression Recognition" on IEEE Transactions on Affective computing (2014), aiming at the intra-class changes in the process of facial expression recognition, it is proposed to use training sample re- Construct an intra-class change feature map, and recognize facial expressions by extracting the difference information between it and different expression images; however, this technical solution does not distinguish between expressionless faces and specific expressions when extracting intra-class change features

[0005] "Modifiedclassification and regression tree for facial expression recognition with using difference expression images" published by Du Lingshuang et al. on Electronics letters (2017) proposes facial expression recognition based on the differential image information between a specific expression face and an expressionless face; However, the technical solution is to directly use image difference for recognition, which will lose a lot of information. Although the intra-class variation features are extracted, it is easy to lose some important information that can be used for recognition.

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