Facial Emotion Recognition Using an Ensemble of Multi-Level Convolutional Neural Networks

被引：33

作者：

Hai-Duong Nguyen ^{[1
]}

Yeom, Soonja ^{[2
]}

Lee, Guee-Sang ^{[1
]}

Yang, Hyung-Jeong ^{[1
]}

Na, In-Seop ^{[3
]}

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] Chosun Univ, Software Convergence Educ Inst, Gwangju, South Korea

来源：

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE | 2019年 / 33卷 / 11期

基金：

新加坡国家研究基金会;

关键词：

Ensemble model; facial emotion recognition in the wild; FER2013; hierarchical features; multi-level convolutional neural networks;

D O I：

10.1142/S0218001419400159

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Emotion recognition plays an indispensable role in human-machine interaction system. The process includes finding interesting facial regions in images and classifying them into one of seven classes: angry, disgust, fear, happy, neutral, sad, and surprise. Although 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 paper, we used multi-level features in a convolutional neural network for facial expression recognition. Based on our observations, we introduced various network connections to improve the classification task. By combining the proposed network connections, our method achieved competitive results compared to state-of-the-art methods on the FER2013 dataset.

引用

页数：17

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