Facial Expression Recognition Using a Multi-level Convolutional Neural Network

被引：0
|
作者：
Hai-Duong Nguyen [1 ]
Yeom, Soonja [2 ]
Oh, Il-Seok [3 ]
Kim, Kyoung-Min [4 ]
Kim, Soo-Hyung [1 ]
机构：
[1] Chonnam Natl Univ, Sch Elect & Comp Engn, Gwangju, South Korea
[2] Univ Tasmania, Sch Engn & ICT, Hobart, Tas, Australia
[3] Chonbuk Natl Univ, Dept Comp Sci & Engn, Gwangju, South Korea
[4] Chonbuk Natl Univ, Dept Elect & Semicond Engn, Gwangju, South Korea
来源：
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE (ICPRAI 2018) | 2018年
基金：
新加坡国家研究基金会;
关键词：
facial expression recognition in the wild; FER2013; hierarchical features; multi-level convolutional neural networks;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Even though many breakthroughs have been made in image classification, especially in facial expression recognition, this research area is still challenging in terms of wild sampling environment. In this work, we carry out a study of multi-level features in a convolutional neural network for facial expression recognition. Based on our observations, we introduce a model by combining a hierarchy of features intentionally to improve the classification task. Our model was evaluated on the FER2013 dataset and achieved a comparable performance to the current state-of-the-art methods.
引用
收藏
页码：217 / 221
页数：5
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