Facial expression recognition method based on random forests

Abstract

The invention discloses a facial expression recognition method based on random forests. The facial expression recognition method based on the random forests comprises the step of extraction of a displacement feature of an AAM, the step of extraction of AUs in a facial expression sequence, the step of training of a facial expression classification model and the step of facial expression recognition. According to the facial expression recognition method, the novel AAM displacement feature is provided to be used for training and learning the AUs, and finally facial expression recognition is carried out by depending on the AUs. Compared with other feature representations in identification of the same classification, the facial expression recognition method based on the random forests better describes expression information and changing process information contained in the expression sequence. The random forests are used for facial expression recognition for the first time, and the random forests in the method have a better classified recognition effect in the field compared with a frequently used support vector machine (SVM) method at present. For the aspect of CK and AU recognition of databases, the facial expression recognition method based on the random forests can achieve a perfect recognition effect.

Description

Technical field [0001] The invention relates to a face expression recognition method, in particular to a face expression recognition method based on random forest. Background technique [0002] Facial expressions, as important non-verbal information, can provide very rich information and effectively supplement language communication. A person's emotions are often directly reflected in facial expressions. Let the computer automatically recognize facial expressions, which is conducive to the computer to better interact with people and provide humanized services. Facial expression recognition can be widely used in many fields, such as medical treatment, human-computer interaction (HCI), video games, data-driven animation, etc. In the past two decades, facial expression recognition has attracted more and more research attention, and a variety of advanced algorithms have been proposed. [1] . Facial expressions have the following characteristics: (1) Subtlety. This is reflected in ...

AI Technical Summary

Problems solved by technology

(3) Facial expressions are complex and changeable

(4) Susceptible to factors such as posture and illumination

Although facial expression recognition has been studied for a long time and many solutions have been proposed, they are still in the stage of experimental research, the effect of practical application is not ideal, and there is still room for further improvement in the recognition effect

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