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Frontiers in Signal Processing
FSP > Volume 4, Number 4, October 2020

A Robust Improved Network for Facial Expression Recognition

Download PDF  (658.4 KB)PP. 81-87,  Pub. Date:August 10, 2020
DOI: 10.22606/fsp.2020.44001

Author(s)
Hao Gao, Bo Ma
Affiliation(s)
College of Electrical & Information Engineering, Southwest Minzu University, Chengdu 610041, China
College of Electrical & Information Engineering, Southwest Minzu University, Chengdu 610041, China
Abstract
With the development of deep learning, even important progress has been made in the field of image classification and recognition. But facial expression recognition still faces many problems. This article is an experiment on the FER2013 dataset, the purpose is to get the facial expression attributes from the facial image. Because the pictures in this dataset have low resolution, and some pictures have no faces at all. This reduces the accuracy of facial expression recognition. In this paper, we propose a robust improved model. In this model, we introduce attention mechanism and separable convolution to improve the extraction of image features, and use data argumentation techniques to enhance the generalization ability of the model. The model obtained 65.2% test set accuracy on the FER2013 dataset.
Keywords
attention mechanism, separable convolution, FER2013
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