Facial Expression Recognition Using Residual Masking Network
This work addresses facial expression recognition for human-computer interaction applications, representing an incremental improvement.
The paper tackled facial expression recognition by proposing a Residual Masking Network that uses a segmentation network to refine feature maps, achieving state-of-the-art accuracy on FER2013 and VEMO datasets.
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.