xLSTM-FER: Enhancing Student Expression Recognition with Extended Vision Long Short-Term Memory Network
<|reference_start|>xLSTM-FER: Enhancing Student Expression Recognition with Extended Vision Long Short-Term Memory Network: Student expression recognition has become an essential tool for assessing learning experiences and emotional states. This paper introduces xLSTM-FER, a novel architecture derived from the Extended Long Short-Term Memory (xLSTM), designed to enhance the accuracy and efficiency of expression recognition through advanced sequence processing capabilities for student facial expression recognition. xLSTM-FER processes input images by segmenting them into a series of patches and leveraging a stack of xLSTM blocks to handle these patches. xLSTM-FER can capture subtle changes in real-world students' facial expressions and improve recognition accuracy by learning spatial-temporal relationships within the sequence. Experiments on CK+, RAF-DF, and FERplus demonstrate the potential of xLSTM-FER in expression recognition tasks, showing better performance compared to state-of-the-art methods on standard datasets. The linear computational and memory complexity of xLSTM-FER make it particularly suitable for handling high-resolution images. Moreover, the design of xLSTM-FER allows for efficient processing of non-sequential inputs such as images without additional computation.<|reference_end|>
@article{huang2024xlstm-fer:,
    title={xLSTM-FER: Enhancing Student Expression Recognition with Extended Vision
  Long Short-Term Memory Network},
    author={Qionghao Huang, Jili Chen},
    journal={arXiv preprint arXiv:2410.05074},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05074},
    primaryClass={cs.CV}
}