Facial expression recognition method based on facial key points and deep neural network

Abstract

The invention discloses a facial expression recognition method based on facial key points and a deep neural network. The facial expression recognition method can be used for realizing real-time detection of facial expressions. The method mainly comprises the following steps: acquiring a facial expression data set, extracting 68 facial key points through preprocessing, and dividing eye, nose and mouth areas so as to respectively obtain multi-path image data and key point coordinate data; training an expression classification network, extracting features from the image data and the coordinate data by adopting a convolutional layer and an image convolutional layer respectively, and outputting a classification result; and evaluating the recognition effect of the network on the facial expression classification data set. Compared with the current main facial expression classification algorithm, the method provided by the invention obtains higher average classification accuracy on the premise of ensuring the recognition real-time performance, and is a high-quality facial expression recognition algorithm.

Description

technical field [0001] The invention relates to a facial expression recognition method based on facial key points and a deep neural network, which is suitable for the technical field of facial expression recognition in computer vision. Background technique [0002] The intelligent recognition of facial expressions has always been an important scientific research direction and a hot topic. Facial expression analysis has a wide range of application scenarios, has a significant effect on improving the quality of human life, and is of great research value. Specific application areas include but are not limited to: in social public areas, using video analysis technology to achieve multi-target tracking and expression analysis, timely identify potential dangers in public environments, and strengthen public safety control; identify and analyze motor vehicle drivers' facial expressions to judge Whether the driver is fatigued or drunk to reduce the incidence of traffic accidents; wi...

AI Technical Summary

Problems solved by technology

Although the facial expression analysis method based on deep learning has achieved good results, there are still some problems in the current method, including: blindly stacking convolutional layers will make it difficult for the model to complete the facial expression analysis in a very short time. The extraction and analysis of faces cannot meet the real-time requirements in actual use scenarios; there is a lack of preprocessing work on facial images, usually after extracting the position of the face in the entire picture, it is simply cut and used as the input of the deep neural network , the network needs to analyze the elements that affect the expression from the whole image by itself, but ignores the effective extraction of facial key point information, which limits the accurate recognition of micro-expressions

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