A Case Study of EmoNeXt-Tiny Without Self-Attention for Facial Emotion Recognition

Online First: 24/06/2026

Các tác giả

Email tác giả liên hệ:

tnminh.cdktcn@khanhhoa.edu.vn

DOI:

https://doi.org/10.54644/jte.2026.2109

Từ khóa:

Facial Emotion Recognition, ConvNeXt-Tiny, Spatial Transformer Network, Squeeze-and-Excitation, Self-Attention, EmoNeXt-Tiny

Tóm tắt

Facial emotion recognition (FER) on FER2013 remains challenging due to low-resolution grayscale images, class imbalance, and limited data diversity. This study presents a controlled Tiny-scale ablation of EmoNeXt-Tiny to isolate the contribution of the self-attention (SA) regularization term. Specifically, SA regularization is removed from the objective function, while all other components and conditions are preserved, including the ConvNeXt-Tiny backbone, the Spatial Transformer Network (STN), Squeeze-and-Excitation (SE) modules, the preprocessing pipeline, and the training/evaluation protocol. Experiments are conducted on FER2013 using the official train/validation/test split. Under an identical setup, the SA-free variant achieves 72.95% top-1 test accuracy, compared with 73.34% for the full EmoNeXt-Tiny and 71.99% for the ConvNeXt-Tiny baseline. Because the present study reports a single controlled run per configuration (i.e., without multi-seed repetition), the 0.39 percentage-point gap should be interpreted as a preliminary observation that may fall within random variance. Within this constraint, the findings indicate diminishing returns from SA regularization in this Tiny regime once geometric alignment and channel re-weighting are already incorporated. In addition, the SA-free model simplifies optimization by removing an auxiliary loss component and its associated tuning burden. Overall, an STN+SE-enhanced ConvNeXt-Tiny without SA offers a practical accuracy–complexity trade-off for resource-constrained FER applications.

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Tiểu sử của Tác giả

Ngoc Minh Tran, Nha Trang College of Technology, Vietnam

Ngoc Minh Tran works in the Information Technology Department, Faculty of Electrical - Electronics at Nha Trang College of Technology since 2009 as a lecturer. He earned a Master’s degree in Information Technology from Hanoi University of Science and Technology in 2012, specialized in teaching software development, web, and mobile applications, has been a member, consultant, editor, and author for technology communities such as CodeProject and DZone since 2016. In free time, he writes articles sharing professional experiences and insights on various fields on the personal blog ngocminhtran.com. Currently, he focuses on researching the application of Deep Learning to enhance vocational education activities under the supervision of Viet-Tuan Le, co-author of this paper.

Email: tnminh.cdktcn@khanhhoa.edu.vn. ORCID:  https://orcid.org/0009-0006-6220-5134

Viet Tuan Le, Ho Chi Minh City Open University, Vietnam

Viet Tuan Le received the Ph.D. degree in computer science and engineering from the Sejong University, Korea, in 2024. He is currently an assistant professor within the Faculty of Information Technology, Ho Chi Minh City Open University, Vietnam. His research interests include diverse network architectures for video anomaly detection and generative models.

Email: tuan.lv@ou.edu.vn. ORCID:  https://orcid.org/0000-0002-2289-8128

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Tải xuống

Đã Xuất bản

2026-06-24

Cách trích dẫn

[1]
Ngoc Minh Tran và Viet Tuan Le, “A Case Study of EmoNeXt-Tiny Without Self-Attention for Facial Emotion Recognition: Online First: 24/06/2026”, JTE, tháng 6 2026.

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