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

• URL: http://arxiv.org/abs/2410.05074v1
• Date: Mon, 7 Oct 2024 14:29:24 GMT
• Title: xLSTM-FER: Enhancing Student Expression Recognition with Extended Vision Long Short-Term Memory Network
• Authors: Qionghao Huang, Jili Chen,
• Abstract summary: This paper introduces xLSTM-FER, a novel architecture derived from the Extended Long Short-Term Memory (xLSTM) xLSTM-FER processes input images by segmenting them into a series of patches and leveraging a stack of xLSTM blocks to handle these patches. Experiments on CK+, RAF-DF, and FERplus demonstrate the potential of xLSTM-FER in expression recognition tasks.
• Score: 0.8287206589886881
• License: http://creativecommons.org/licenses/by/4.0/
• 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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