FER-YOLO-Mamba: Facial Expression Detection and Classification Based on Selective State Space
<|reference_start|>FER-YOLO-Mamba: Facial Expression Detection and Classification Based on Selective State Space: 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.<|reference_end|>
@article{ma2024fer-yolo-mamba:,
    title={FER-YOLO-Mamba: Facial Expression Detection and Classification Based on
  Selective State Space},
    author={Hui Ma, Sen Lei, Turgay Celik, Heng-Chao Li},
    journal={arXiv preprint arXiv:2405.01828},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01828},
    primaryClass={cs.CV}
}