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.

翻译：人脸表情识别（Facial Expression Recognition, FER）在理解人类情感线索中扮演着关键角色。然而，基于视觉信息的传统FER方法存在预处理、特征提取和多阶段分类流程等局限性，这些不仅增加了计算复杂度，还消耗大量计算资源。考虑到基于卷积神经网络（CNN）的FER方案常难以充分识别嵌入在人脸表情图像中的深层长距离依赖关系，且Transformer具有固有的二次计算复杂度，本文提出了FER-YOLO-Mamba模型，该模型融合了Mamba与YOLO技术原理，以促进人脸表情图像识别与定位的高效协同。在FER-YOLO-Mamba模型中，我们进一步设计了FER-YOLO-VSS双分支模块，该模块结合了卷积层在局部特征提取方面的固有优势，以及状态空间模型（SSMs）在揭示长距离依赖关系方面的卓越能力。据我们所知，这是首个专为人脸表情检测与分类设计的Vision Mamba模型。为评估所提出的FER-YOLO-Mamba模型的性能，我们在两个基准数据集RAF-DB和SFEW上进行了实验。实验结果表明，FER-YOLO-Mamba模型相较于其他模型取得了更优的结果。代码可从https://github.com/SwjtuMa/FER-YOLO-Mamba获取。