Version 1
: Received: 17 August 2024 / Approved: 19 August 2024 / Online: 19 August 2024 (09:53:05 CEST)
How to cite:
Chen, X.; Huang, L. A Lightweight Model Enhancing Facial Expression Recognition with Spatial Bias and Cosine‐Harmony Loss. Preprints2024, 2024081304. https://doi.org/10.20944/preprints202408.1304.v1
Chen, X.; Huang, L. A Lightweight Model Enhancing Facial Expression Recognition with Spatial Bias and Cosine‐Harmony Loss. Preprints 2024, 2024081304. https://doi.org/10.20944/preprints202408.1304.v1
Chen, X.; Huang, L. A Lightweight Model Enhancing Facial Expression Recognition with Spatial Bias and Cosine‐Harmony Loss. Preprints2024, 2024081304. https://doi.org/10.20944/preprints202408.1304.v1
APA Style
Chen, X., & Huang, L. (2024). A Lightweight Model Enhancing Facial Expression Recognition with Spatial Bias and Cosine‐Harmony Loss. Preprints. https://doi.org/10.20944/preprints202408.1304.v1
Chicago/Turabian Style
Chen, X. and Liangyu Huang. 2024 "A Lightweight Model Enhancing Facial Expression Recognition with Spatial Bias and Cosine‐Harmony Loss" Preprints. https://doi.org/10.20944/preprints202408.1304.v1
Abstract
This paper proposes a novel facial expression recognition network called the Lightweight Facial Network with Spatial Bias (LFNSB). The LFNSB model balances model complexity and recognition accuracy. It has two key components: a lightweight feature extraction network (LFN) and a Spatial Bias (SB) module for aggregating global information. The LFN introduces combined channel operations and depthwise convolution techniques, effectively reducing the number of parameters while enhancing feature representation capability. The Spatial Bias module enables the model to focus on local facial features while also capturing the dependencies between different facial regions. Additionally, a novel loss function called Cosine-Harmony Loss is designed. This function optimizes the relative positions of feature vectors in high-dimensional space, resulting in better feature separation and clustering. Experimental results on the AffectNet and RAF-DB datasets show that the proposed LFNSB model performs excellently in facial expression recognition tasks. It achieves high recognition accuracy while significantly reducing the number of parameters, thus substantially lowering model complexity.
Keywords
facial expression recognition; Spatial Bias; Cosine‐Harmony Loss; Lightweight Model
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
