Multi-angle facial expression recognition method and device, equipment and storage medium

Abstract

The invention relates to a multi-angle facial expression recognition method and device, equipment and a storage medium. The method comprises the following steps: constructing a neural network model and a facial expression image training library; according to the convertible convolution module and the multi-angle facial expression image training data, obtaining an output feature map of the multi-angle facial expression image training data; according to the attention module and an output feature map of the multi-angle facial expression image training data, training the neural network model for a plurality of times to obtain a plurality of trained neural network models; according to a preset accuracy calculation algorithm, obtaining a neural network model with the highest accuracy from the plurality of trained neural network models as an optimal neural network model; and obtaining a recognition instruction of a user, the recognition instruction comprising a pre-recognized facial expression image, and obtaining a facial expression recognition result according to the pre-recognized facial expression image and the optimal neural network model.

Description

technical field [0001] The present invention relates to the field of image processing, in particular to a method, device, equipment and storage medium for multi-angle facial expression recognition. Background technique [0002] As one of the important components of the human body, the features of the human face contain a lot of information in the communication process. Among them, facial expressions are one of the important ways for people to express their emotions. Psychologist Mehrabiadu pointed out that facial expressions convey 55% of the useful information in the process of people's communication, while the useful information conveyed by language only accounts for 7%. Through facial expression recognition technology, the level of human-computer interaction can be further improved, and it has broad application prospects in education, psychological diagnosis, public safety and other fields. [0003] Faces collected in the current natural environment will have side faces,...

AI Technical Summary

Problems solved by technology

In response to this problem, the current technical solution is to perform face detection and face correction on the face picture first, and then use the convolutional neural network to perform emotion classification. However, this type of method requires a series of preprocessing of the image, and the operation is relatively complex. cumbersome

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