Abstract
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 this https URL.
Abstract (translated)
面部表情识别（FER）在理解人类情感线索方面起着关键作用。然而，基于视觉信息的传统FER方法存在一些限制，例如预处理、特征提取和多级分类过程。这些不仅增加了计算复杂度，而且需要大量的计算资源。考虑到基于卷积神经网络（CNN）的FER方案在识别面部表情图像中的深度、长距离依赖方面常常表现不足，以及Transformer的固有二次计算复杂度，本文提出了FER-YOLO-Mamba模型，该模型将Mamba和YOLO技术的原则整合起来，以促进面部表情图像识别和定位的高效协调。在FER-YOLO-Mamba模型中，我们进一步设计了一个FER-YOLO-VSS双分支模块，将局部特征提取的卷积层固有优势与State Space Models（SSM）在揭示长距离依赖的非凡能力相结合。据我们所知，这是第一个针对面部表情检测和分类设计的视觉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上进行了实验：RAF-DB和SFEW。实验结果表明，FER-YOLO-Mamba模型取得了比其他模型更好的效果。代码可以从该链接的URL中获取。
URL
https://arxiv.org/abs/2405.01828
