Human face emotion identifying method based on Bayes fusion sparse representation classifier

Abstract

The invention discloses a human face emotion identifying method. The method includes pretreatment, image division, feature extraction, classification and classification result fusion of human face expression pictures. The method is characterized in that a complete human face expression picture is divided into four sub images (corresponding to the forehead, the eyes, the nose and the mouth) according to the distribution of the five sense organs; a sparse representation classifier is utilized for classifying the sub pictures and the original picture so as to obtain five possible classification results; finally the weighing Bayes fusion decision theory is utilized for adjusting the weight distribution of the five sense organs and the similarity and difference between expressions are both taken into consideration. The method has advantages of being simple to implement, good in noise prevention robustness and being capable of handling complicated human face expression identification well and improving human face expression identification accuracy and the like.

Description

technical field [0001] The invention relates to a pattern recognition technology, in particular to a human face emotion recognition method based on a Bayesian fusion sparse representation classifier. Background technique [0002] As human-computer interaction becomes the craze of the new century, facial emotion recognition is also playing an increasingly important role. Many electronic devices now have a need to improve the ability to understand human emotions. For example: if a nursing robot has the ability to continuously monitor the patient's emotional state, it can give the patient appropriate care and quickly respond to critical situations. In addition, if the owner of the smart home is detected to express negative emotions, the smart home system can choose to play the owner's favorite music or speak positive words in response. [0003] It is precisely because of the wide application of facial emotion recognition that many methods for facial emotion recognition have b...

AI Technical Summary

Problems solved by technology

However, the current integration is mostly feature-based integration, and it has not noticed that the contribution of facial features and other parts to the recognition degree is not the same in the process of facial emotion recognition.

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