  Facial Expression Recognition (FER) plays a pivotal role in understanding
human emotional cues. However, traditional FER methods based on visual
information have some limitations, such as preprocessing, feature extraction,
and multi-stage classification procedures. These not only increase
computational complexity but also require a significant amount of computing
resources. Considering Convolutional Neural Network (CNN)-based FER schemes
frequently prove inadequate in identifying the deep, long-distance dependencies
embedded within facial expression images, and the Transformer's inherent
quadratic computational complexity, this paper presents the FER-YOLO-Mamba
model, which integrates the principles of Mamba and YOLO technologies to
facilitate efficient coordination in facial expression image recognition and
localization. Within the FER-YOLO-Mamba model, we further devise a FER-YOLO-VSS
dual-branch module, which combines the inherent strengths of convolutional
layers in local feature extraction with the exceptional capability of State
Space Models (SSMs) in revealing long-distance dependencies. To the best of our
knowledge, this is the first Vision Mamba model designed for facial expression
detection and classification. To evaluate the performance of the proposed
FER-YOLO-Mamba model, we conducted experiments on two benchmark datasets,
RAF-DB and SFEW. The experimental results indicate that the FER-YOLO-Mamba
model achieved better results compared to other models. The code is available
from https://github.com/SwjtuMa/FER-YOLO-Mamba.
