4.6 Article

Attention-Guided Network Model for Image-Based Emotion Recognition

期刊

APPLIED SCIENCES-BASEL
卷 13, 期 18, 页码 -

出版社

MDPI
DOI: 10.3390/app131810179

关键词

attention module; emotion recognition; deep learning; digital health; mental well-being; network prediction analysis

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This study developed an attention module with a guidance strategy for facial emotion recognition. The module showed better performance compared to other popular attention modules, highlighting the effectiveness of guiding the model.
Featured Application This work is being developed as part of a closed-loop system to be used in the therapeutic treatment of people with autism spectrum disorder.Abstract Neural networks are increasingly able to outperform traditional machine learning and filtering approaches in classification tasks. However, with the rise in their popularity, many unknowns still exist when it comes to the internal learning processes of the networks in terms of how they make the right decisions for prediction. As a result, in this work, different attention modules integrated into a convolutional neural network coupled with an attention-guided strategy were examined for facial emotion recognition performance. A custom attention block, AGFER, was developed and evaluated against two other well-known modules of squeeze-excitation and convolution block attention modules and compared with the base model architecture. All models were trained and validated using a subset from the OULU-CASIA database. Afterward, cross-database testing was performed using the FACES dataset to assess the generalization capability of the trained models. The results showed that the proposed attention module with the guidance strategy showed better performance than the base architecture while maintaining similar results versus other popular attention modules. The developed AGFER attention-integrated model focused on relevant features for facial emotion recognition, highlighting the efficacy of guiding the model during the integral training process.

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