Facial Expression Recognition Using Residual Masking Network

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...

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Bibliographic Details
Published in2020 25th International Conference on Pattern Recognition (ICPR) pp. 4513 - 4519
Main Authors Pham, Luan, Vu, The Huynh, Tran, Tuan Anh
Format Conference Proceeding
LanguageEnglish
Published IEEE 10.01.2021
Subjects
Buildings
Deep architecture
Face recognition
Facial Expression Recognition
Human computer interaction
Masking Idea
Residual Masking Network
Residual neural networks
Task analysis
Testing
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Summary: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.
DOI:10.1109/ICPR48806.2021.9411919