Classroom facial expression recognition method based on improved RT-DETR model

By improving the CAFI, MCFEConv, and RepC3_LWGA modules of the RT-DETR model, the problems of insufficient small-scale facial feature extraction and background interference in classroom facial expression recognition were solved, and high-precision expression recognition was achieved in complex classroom environments.

CN121963268APending Publication Date: 2026-05-01CHANGCHUN NORMAL UNIV
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Patent Information

Application Number
CN202511849996.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-09
Publication Date
2026-05-01

AI Technical Summary

Technical Problem

Existing RT-DETR models suffer from insufficient small-scale facial feature extraction and a lack of synergistic fusion between the fine-grained features required for expression classification and the global features of the detection task in classroom facial expression recognition tasks. This results in low recognition accuracy and makes it difficult to apply effectively in complex classroom environments.

Method used

The improved RT-DETR model enhances the ability to extract and filter facial expression features in the classroom by designing a CAFI module in the backbone network and an MCFEConv convolutional feature enhancement module and a RepC3_LWGA module in the head network. It combines local feature enhancement, spatial attention, adaptive positional encoding and Transformer global modeling to suppress background interference and enhance the robustness and discriminativeness of features.

Benefits of technology

It significantly improves the stability and accuracy of facial expression recognition in the classroom, and can effectively identify subtle changes in facial expressions in classroom scenarios with multiple students, long distances, and complex lighting conditions. It solves the problems of insufficient feature expression and low recognition accuracy of traditional models in complex backgrounds.

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Abstract

The invention discloses a classroom facial expression recognition method based on an improved RT-DETR model. The method comprises the following steps: 1, obtaining a data set of classroom facial expression images; 2, preprocessing the data set by using a computer; 3, training a data set by using a classroom facial expression recognition detection model based on an improved RT-DETR model; 4, training the classroom facial expression recognition detection model to obtain an optimal detection model; and 5, performing real-time identification detection on the classroom facial expressions of the students in combination with monitoring and an optimal detection model. According to the invention, the CAFI module is designed in the backbone network, so that the classroom facial expression features can be extracted and screened more accurately; an MCFEEConv convolution feature enhancement module is designed in the head network, and the feature expression ability of faces with different distances and different sizes is improved; a RepC3LWGA module is designed in a head network, and joint modeling of local convolution features and global context features is achieved.
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