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：人脸表情识别在理解人类情感线索中发挥着关键作用。然而，传统的基于视觉信息的人脸表情识别方法存在局限性，例如预处理、特征提取和多阶段分类流程。这些步骤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的人脸表情识别方案在识别面部表情图像中嵌入的深层长距离依赖关系时往往表现不足，且Transformer固有的二次计算复杂度问题，本文提出了FER-YOLO-Mamba模型，该模型整合了Mamba与YOLO技术的原理，以实现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与状态空间模型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。为了评估所提出的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。