Facial Expression Recognition Using Residual Masking Network

• URL: http://arxiv.org/abs/2603.05937v1
• Date: Fri, 06 Mar 2026 06:08:41 GMT
• Title: Facial Expression Recognition Using Residual Masking Network
• Abstract summary: We propose a novel Masking idea to boost the performance of CNN in facial expression task.<n>In experiments, we combine the ubiquitous Deep Residual Network and Unet-like architecture to produce a Residual Masking Network.<n>The proposed method holds state-of-the-art (SOTA) accuracy on the well-known FER2013 and private VEMO datasets.
• Score: 0.28675177318965045
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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