xLSTM-FER: Enhancing Student Expression Recognition with Extended Vision Long Short-Term Memory Network

Qionghao Huang; Jili Chen

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arXiv · 2410.05074

xLSTM-FER: Enhancing Student Expression Recognition with Extended Vision Long Short-Term Memory Network

Qionghao Huang·Jili Chen

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Abstract

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.

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BibTeX RIS

Qionghao Huang, Jili Chen. 2024-10-07. xLSTM-FER: Enhancing Student Expression Recognition with Extended Vision Long Short-Term Memory Network. https://arxiv.org/abs/2410.05074

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