Global Multiple Extraction Network for Low-Resolution Facial Expression Recognition

• URL: http://arxiv.org/abs/2511.05938v1
• Date: Sat, 08 Nov 2025 09:10:35 GMT
• Title: Global Multiple Extraction Network for Low-Resolution Facial Expression Recognition
• Authors: Jingyi Shi,
• Abstract summary: We propose a novel global multiple extraction network (GME-Net) for low-resolution facial expression recognition.<n>Our GME-Net is capable of extracting expression-related discriminative features.
• Score: 0.6368598462231703
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Facial expression recognition, as a vital computer vision task, is garnering significant attention and undergoing extensive research. Although facial expression recognition algorithms demonstrate impressive performance on high-resolution images, their effectiveness tends to degrade when confronted with low-resolution images. We find it is because: 1) low-resolution images lack detail information; 2) current methods complete weak global modeling, which make it difficult to extract discriminative features. To alleviate the above issues, we proposed a novel global multiple extraction network (GME-Net) for low-resolution facial expression recognition, which incorporates 1) a hybrid attention-based local feature extraction module with attention similarity knowledge distillation to learn image details from high-resolution network; 2) a multi-scale global feature extraction module with quasi-symmetric structure to mitigate the influence of local image noise and facilitate capturing global image features. As a result, our GME-Net is capable of extracting expression-related discriminative features. Extensive experiments conducted on several widely-used datasets demonstrate that the proposed GME-Net can better recognize low-resolution facial expression and obtain superior performance than existing solutions.

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