Lightweight facial expression recognition based on joint model compression

Guangming Song; Chunxiao Fan

doi:10.1117/12.3045275

16 January 2025 Lightweight facial expression recognition based on joint model compression

Guangming Song, Chunxiao Fan

Author Affiliations +

Proceedings Volume 13447, International Conference on Mechatronics and Intelligent Control (ICMIC 2024); 134470O (2025) https://doi.org/10.1117/12.3045275
Event: International Conference on Mechatronics and Intelligent Control (ICMIC 2024), 2024, Wuhan, China

Abstract

In this paper, we propose a lightweight facial expression recognition algorithm based on joint model compression. Firstly, we propose a self-distillation method based on facial expression recognition, which not only considers the overall output of the model but also focuses on the prediction results of each facial expression category, thereby improving the performance and generalization ability of the facial expression recognition model. Secondly, in order to further compress the facial expression recognition model, we apply a channel pruning method based on transformed L₁ regularization. This pruning method imposes non-convex constraints on the parameters of the facial expression recognition model through transformations of the parameters in the L₁ norm penalty term, achieving more accurate sparse regularization and effectively preventing overfitting of the model. Lastly, the pruned network is quantified to low-bit to obtain the final lightweight facial expression recognition model. The proposed method achieves 73.25% on the FER2013 dataset, with a parameter compression rate of approximately 50%, which achieves an effective lightweight.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guangming Song and Chunxiao Fan "Lightweight facial expression recognition based on joint model compression", Proc. SPIE 13447, International Conference on Mechatronics and Intelligent Control (ICMIC 2024), 134470O (16 January 2025); https://doi.org/10.1117/12.3045275

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE