Robust continuous emotion tracking method based on deep learning

Abstract

The invention relates to a robust continuous emotion tracking method based on deep learning. The method comprises the steps that (1) a training sample is constructed, and a normalization model and a continuous emotion tracking model are trained; (2) an expression image is acquired and preprocessed, the expression image obtained after being preprocessed is sent to the trained normalization model, and an expression picture with standard illumination and a standard head posture is obtained; (3) a standard image obtained after normalization is used as input of the continuous emotion tracking model, expression-related features are automatically extracted and input through the continuous emotion tracking model, and a tracking result of a current frame is generated according to time sequence information; and the steps (2) and (3) are repeated till a whole continuous emotion tracking process is completed. The method based on deep learning is adopted to construct an emotion recognition model so as to realize continuous emotion tracking and prediction, the method has robustness on illumination and posture changes, and the time sequence information of expressions can be fully utilized to track the emotion of a current user more stably based on historical emotion features.

Description

technical field [0001] The invention relates to the fields of human-computer interaction, image processing, and artificial intelligence, and in particular to a robust deep learning-based continuous emotion tracking method. Background technique [0002] The purpose of emotional intention understanding is to enable computers to have higher human-like intelligence and provide a more natural human-computer interaction experience by endowing computers with the ability to recognize, understand, and recognize human emotions. With the popularity of computer equipment, webcams and other equipment, emotion recognition based on visual channels has become the most effective means to analyze user emotions. [0003] Most of the current emotion recognition methods divide emotions into several basic categories, such as happiness, anger, sadness, surprise, etc., so that the emotion recognition problem is transformed into a classification problem, and then through a well-designed artificial f...

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Problems solved by technology

The patent "A Method for Facial Expression Recognition Based on Deep Learning" (public number: CN103793718A) discloses a method of using DBN to learn emotional features on two-dimensional pictures and classify them. This method has achieved good results in experim

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