用分割网络增强注意力，提升表情识别准确率
Facial Expression Recognition Using Residual Masking Network
- 引入掩码机制，让CNN聚焦关键面部区域
- 在FER2013和VEMO数据集上达当前最优性能
- 适合关注表情识别与注意力机制的研究者
自动面部表情识别（FER）在人机交互中应用广泛。为提升FER性能，本文聚焦于结合注意力机制的深度网络架构。提出一种新颖的掩码思想，利用分割网络对特征图进行精炼，使网络更关注与表情相关的有效信息，从而做出更准确判断。实验中将通用的深度残差网络与类似Unet的结构结合，构建出残差掩码网络。该方法在知名的FER2013数据集和自有的VEMO数据集上均达到当前最优（SOTA）准确率。源代码已公开于https://github.com/phamquiluan/ResidualMaskingNetwork。
原文摘要 · Abstract (English)
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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