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A novel multi-scale facial expression recognition algorithm based on improved Res2Net for classroom scenes

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Abstract

Facial expression recognition under classroom scenes can help the teacher to understand students’ classroom learning status and improve teaching effectiveness. Aiming at the problem of low expression recognition accuracy in classroom scenarios, a novel multi-scale facial expression recognition algorithm based on improved Res2Net is proposed. Firstly, a bi-directional residual BiRes2Net module is proposed to achieve bi-directional multi-scale expression feature extraction at the fine-grained level, while a short-directed connection path is introduced to make the network have the self-closing capability and avoid extracting redundant information of expressions; Then the Fine-Grained Coordinate Attention (FGCA) mechanism is embedded to extract expression spatial location features and channel features at a fine-grained level by making full use of the prior knowledge of facial expressions; Finally, a multi-classification Focalloss loss function is used to alleviate the imbalance of expression data, and different weights are assigned to expression samples with different recognition difficulty so that the network is biased towards difficult sample feature extraction. The experimental results show that the recognition accuracy of the  proposed method is 79.47%, 94.06%, and 96.67% in RAF-DB, JAFFE, and CK+ datasets respectively, and up to 72.71% in real classroom scenes, which are better than other comparative algorithms significantly.

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Data availability statements

The classroom scenario dataset we created is available from the corresponding author on reasonable request. The access links of the publicly available dataset are as follows:

RAF-DB: http://www.whdeng.cn/RAF/model1.html

JAFFE: http://www.kasrl.org/jaffe.html

CK+: http://www.consortium.ri.cmu.edu/ckagree/

Abbreviations

BiRes2Net:

Bi-directional residual Res2Net Module

FGCA:

Fine-Grained Coordinate Attention

RAF-DB:

Real-world Affective Faces Database

JAFFE:

The Japanses Female Facial Expression Database

CK +:

The Extended Cohn-Kanade Dataset.

EMFACS:

Emotional Facial Action Coding System

SCN:

Self-Cure Convolutional Neural Network

ICID:

Inter-Domain Facial Expression Recognition Feature Fusion Network

IC:

Intra-category Common feature

ID:

Inter-category Distinction feature

FDRL:

Feature Decomposition and Reconstruction Learning

FDN:

Feature Decomposition Network

FRN:

Feature Reconstruction Network

DLP-CNN:

Deep locality-preserving CNN

DMFA-ResNet:

deep multiscale fusion attention residual network

CERT:

the Computer Expression Recognition Toolbox

SE:

Squeeze-and-Excitation

CA:

Coordinate Attention

NE:

Natural

DI:

Disgust

FE:

Fear

AN:

Anger

HA:

Happiness

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Acknowledgements

This work was supported in part by Postgraduate Innovation Fund Project of Xi’an Polytechnic University (chx2022012).

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Authors and Affiliations

  1. School of Electronics Information, Xi’an Polytechnic University, Xi’an, 710048, China

    Meihua Gu, Jing Feng & Yalu Chu

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  1. Meihua Gu
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Contributions

Meihua Gu and Jing Feng designed the research, performed the research, Yalu Chu analyzed the data, all authors contributed to the writing and revisions.

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Correspondence to Meihua Gu.

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Gu, M., Feng, J. & Chu, Y. A novel multi-scale facial expression recognition algorithm based on improved Res2Net for classroom scenes. Multimed Tools Appl 83, 16525–16542 (2024). https://doi.org/10.1007/s11042-023-16115-0

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