Facial Expression Recognition in Students Using an Extended Vision Long Short-Term Memory Network (xLSTM-FER)

Keskeiset käsitteet

xLSTM-FER, a novel architecture based on Extended Long Short-Term Memory (xLSTM), offers a computationally efficient and highly accurate method for recognizing student facial expressions, outperforming existing CNN and ViT-based approaches.

Tiivistelmä

Bibliographic Information:

Huang, Q., & Chen, J. (2024). xLSTM-FER: Enhancing Student Expression Recognition with Extended Vision Long Short-Term Memory Network. arXiv preprint arXiv:2410.05074v1.

Research Objective:

This paper introduces xLSTM-FER, a novel deep learning architecture for facial expression recognition (FER) in students, aiming to address the limitations of existing methods like CNNs and ViTs in capturing long-range dependencies and handling high-resolution images efficiently.

Methodology:

xLSTM-FER leverages an extended version of Long Short-Term Memory (xLSTM) networks. It segments input images into patches, encodes them using stacked xLSTM blocks to capture spatial-temporal dynamics, and employs a classification head for emotion prediction. The model utilizes a memory matrix within the mLSTM layer for enhanced parallel processing and incorporates a path transfer mechanism for a comprehensive image representation.

Key Findings:

• xLSTM-FER achieves state-of-the-art accuracy on three benchmark datasets: CK+, RAF-DB, and FERplus.
• It demonstrates superior performance compared to CNN-based methods like FER-GCN and EAC, and ViT-based models like ViT and MA-Net.
• The model's linear computational and memory complexity makes it highly efficient for processing high-resolution images, crucial for capturing subtle facial expressions.

Main Conclusions:

xLSTM-FER presents a promising approach for student FER, offering high accuracy and computational efficiency. Its ability to capture long-range dependencies and handle high-resolution images effectively addresses key limitations of existing methods.

Significance:

This research significantly contributes to the field of computer vision, particularly in FER, by introducing a novel architecture that balances accuracy and efficiency. It has practical implications for educational technology, enabling more effective assessment of student engagement and emotional states.

Limitations and Future Research:

While xLSTM-FER shows promising results, further research can explore its generalization capabilities across diverse ethnicities and lighting conditions. Investigating its integration with multimodal learning, incorporating audio and contextual information, could further enhance its robustness and accuracy in real-world educational settings.

Tilastot

xLSTM-FER achieves 100% accuracy on the CK+ dataset.
xLSTM-FER achieves 87.06% accuracy on the RAF-DB dataset, a 14% improvement over previous state-of-the-art methods.
xLSTM-FER achieves 88.94% accuracy on the FERplus dataset, a 4.5% improvement over previous state-of-the-art methods.

Lainaukset

"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."

Tärkeimmät oivallukset

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

by Qionghao Hua... klo arxiv.org 10-08-2024

https://arxiv.org/pdf/2410.05074.pdf