A method and system for constructing an expression recognition model

Abstract

The invention relates to a method and system for constructing an expression recognition model, and relates to the field of data processing. The method includes S1: acquiring a source image and performing preprocessing to obtain a preprocessed image; S2: constructing a deep learning model according to the source image and the preprocessing image, introducing migration learning to perform weight training on the deep learning model, and obtaining Image pre-training model; S3: Obtain a fine-tuning model according to the image pre-training model; S4: Use the fine-tuning model to perform fine-tuning model training on the preprocessed image to obtain an expression recognition model. This solution solves how to improve the expression recognition model. The technical problem of accuracy is applicable to expression recognition.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a method and system for constructing an expression recognition model. Background technique [0002] At present, the construction methods of facial expression recognition models are mainly divided into two categories: one is based on the facial action coding system, which detects facial muscle movements and constructs the mapping relationship between them and emotions to achieve the purpose of expression recognition; It adopts the method of deep learning, through the end-to-end learning method, automatically extracts the characteristics of human expressions, and performs expression recognition. [0003] At present, the facial expression recognition model construction method is difficult to achieve a high accuracy rate. The main problems are as follows: the accuracy of expression recognition based on deep learning often relies on a huge training data set, and the expression data set ...

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Problems solved by technology

[0003] At present, the facial expression recognition model construction method is difficult to achieve a high accuracy rate. The main problems are as follows: the accuracy of expression recognition based on deep learning often relies on a huge training data set, and the expression data set is difficult to construct and obtain on a large scale. Training requirements, at the same time, lighting factors, obstacle occlusion or face posture are all interference factors that affect facial expression recognition

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