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

TL;DR

This paper presents xLSTM-FER, a novel neural network architecture that improves student facial expression recognition by capturing spatial-temporal features efficiently, demonstrating superior accuracy and computational efficiency on standard datasets.

Contribution

The paper introduces xLSTM-FER, an innovative architecture that enhances expression recognition by processing image patches with extended LSTM blocks, offering improved accuracy and efficiency over existing methods.

Findings

xLSTM-FER outperforms state-of-the-art methods on CK+, RAF-DF, and FERplus datasets.

It captures subtle facial expression changes effectively in real-world scenarios.

The model maintains linear computational complexity, suitable for high-resolution images.

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.