Development of deep learning-based facial expression recognition system

Heechul Jung; Sihaeng Lee; Sunjeong Park; Byungju Kim; Junmo Kim; Injae Lee; Chunghyun Ahn

doi:10.1109/FCV.2015.7103729

Development of deep learning-based facial expression recognition system

Heechul Jung, Sihaeng Lee, Sunjeong Park, Byungju Kim, Junmo Kim, Injae Lee, Chunghyun Ahn

School of Electronics Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

48 Scopus citations

Abstract

Deep learning is considered to be a breakthrough in the field of computer vision, since most of the world records of the recognition tasks are being broken. In this paper, we try to apply such deep learning techniques to recognizing facial expressions that represent human emotions. The procedure of our facial expression recognition system is as follows: First, face is detected from input image using Haar-like features. Second, the deep network is used for recognizing facial expression using detected faces. In this step, two different deep networks can be used such as deep neural network and convolutional neural network. Consequently, we compared experimentally two types of deep networks, and the convolutional neural network had better performance than deep neural network.

Original language	English
Title of host publication	2015 Frontiers of Computer Vision, FCV 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479917204
DOIs	https://doi.org/10.1109/FCV.2015.7103729
State	Published - 7 May 2015
Event	2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision, FCV 2015 - Mokpo, Korea, Republic of Duration: 28 Jan 2015 → 30 Jan 2015

Publication series

Name	2015 Frontiers of Computer Vision, FCV 2015

Conference

Conference	2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision, FCV 2015
Country/Territory	Korea, Republic of
City	Mokpo
Period	28/01/15 → 30/01/15

Keywords

convolutional neural network
deep learning
deep neural network
Facial expression recognition

Access to Document

10.1109/FCV.2015.7103729

Cite this

@inproceedings{78d0f9b94291472b81e186d88738c04c,

title = "Development of deep learning-based facial expression recognition system",

abstract = "Deep learning is considered to be a breakthrough in the field of computer vision, since most of the world records of the recognition tasks are being broken. In this paper, we try to apply such deep learning techniques to recognizing facial expressions that represent human emotions. The procedure of our facial expression recognition system is as follows: First, face is detected from input image using Haar-like features. Second, the deep network is used for recognizing facial expression using detected faces. In this step, two different deep networks can be used such as deep neural network and convolutional neural network. Consequently, we compared experimentally two types of deep networks, and the convolutional neural network had better performance than deep neural network.",

keywords = "convolutional neural network, deep learning, deep neural network, Facial expression recognition",

author = "Heechul Jung and Sihaeng Lee and Sunjeong Park and Byungju Kim and Junmo Kim and Injae Lee and Chunghyun Ahn",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision, FCV 2015 ; Conference date: 28-01-2015 Through 30-01-2015",

year = "2015",

month = may,

day = "7",

doi = "10.1109/FCV.2015.7103729",

language = "English",

series = "2015 Frontiers of Computer Vision, FCV 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2015 Frontiers of Computer Vision, FCV 2015",

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}

Jung, H, Lee, S, Park, S, Kim, B, Kim, J, Lee, I & Ahn, C 2015, Development of deep learning-based facial expression recognition system. in 2015 Frontiers of Computer Vision, FCV 2015., 7103729, 2015 Frontiers of Computer Vision, FCV 2015, Institute of Electrical and Electronics Engineers Inc., 2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision, FCV 2015, Mokpo, Korea, Republic of, 28/01/15. https://doi.org/10.1109/FCV.2015.7103729

Development of deep learning-based facial expression recognition system. / Jung, Heechul; Lee, Sihaeng; Park, Sunjeong et al.
2015 Frontiers of Computer Vision, FCV 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7103729 (2015 Frontiers of Computer Vision, FCV 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Development of deep learning-based facial expression recognition system

AU - Jung, Heechul

AU - Lee, Sihaeng

AU - Park, Sunjeong

AU - Kim, Byungju

AU - Kim, Junmo

AU - Lee, Injae

AU - Ahn, Chunghyun

PY - 2015/5/7

Y1 - 2015/5/7

N2 - Deep learning is considered to be a breakthrough in the field of computer vision, since most of the world records of the recognition tasks are being broken. In this paper, we try to apply such deep learning techniques to recognizing facial expressions that represent human emotions. The procedure of our facial expression recognition system is as follows: First, face is detected from input image using Haar-like features. Second, the deep network is used for recognizing facial expression using detected faces. In this step, two different deep networks can be used such as deep neural network and convolutional neural network. Consequently, we compared experimentally two types of deep networks, and the convolutional neural network had better performance than deep neural network.

AB - Deep learning is considered to be a breakthrough in the field of computer vision, since most of the world records of the recognition tasks are being broken. In this paper, we try to apply such deep learning techniques to recognizing facial expressions that represent human emotions. The procedure of our facial expression recognition system is as follows: First, face is detected from input image using Haar-like features. Second, the deep network is used for recognizing facial expression using detected faces. In this step, two different deep networks can be used such as deep neural network and convolutional neural network. Consequently, we compared experimentally two types of deep networks, and the convolutional neural network had better performance than deep neural network.

KW - convolutional neural network

KW - deep learning

KW - deep neural network

KW - Facial expression recognition

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U2 - 10.1109/FCV.2015.7103729

DO - 10.1109/FCV.2015.7103729

M3 - Conference contribution

AN - SCOPUS:84937117344

T3 - 2015 Frontiers of Computer Vision, FCV 2015

BT - 2015 Frontiers of Computer Vision, FCV 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision, FCV 2015

Y2 - 28 January 2015 through 30 January 2015

ER -

Development of deep learning-based facial expression recognition system

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Keywords

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