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Facial Expression Recognition Using Residual Masking Network

arXiv 2603.05937 ↗First posted 6 Mar 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

Automatic facial expression recognition (FER) has gained much attention due to its applications in human-computer interaction. Among the approaches to improve FER tasks, this paper focuses on deep architecture with the attention mechanism. We propose a novel Masking idea to boost the performance of CNN in facial expression task. It uses a segmentation network to refine feature maps, enabling the network to focus on relevant information to make correct decisions. In experiments, we combine the ubiquitous Deep Residual Network and Unet-like architecture to produce a Residual Masking Network. The proposed method holds state-of-the-art (SOTA) accuracy on the well-known FER2013 and private VEMO datasets. The source code is available at https://github.com/phamquiluan/ResidualMaskingNetwork.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 28 of its 50 references that are in the map. First posted on arXiv 6 Mar 2026.

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References · 28

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16 more
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