Real-time personalized facial expression recognition system based on deep learning

Injae Lee; Heechul Jung; Chung Hyun Ahn; Jeongil Seo; Junmo Kim; Ohseok Kwon

doi:10.1109/ICCE.2016.7430609

Real-time personalized facial expression recognition system based on deep learning

Injae Lee
, Heechul Jung
, Chung Hyun Ahn
, Jeongil Seo
, Junmo Kim
, Ohseok Kwon

School of Electronics Engineering

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

17 Scopus citations

Abstract

Over the last few years, deep learning has produced breakthrough results in many application fields including speech recognition, image understanding and so on. We try to deep learning techniques for real-time facial expression recognition instead of hand-crafted feature-based methods. The proposed system can recognize human emotions based on facial expressions using a webcam. It can detect faces and recognize users with a distance of 2∼3m for TV environment. And it can determine whether a user is feeling happiness, sadness, surprise, anger, disgust, neutral or any combination of those six emotions. The experimental results show that the proposed method achieves high accuracy. It can be used for various services such as consumer behavior research, usability studies, psychology, educational research, and market research.

Original language	English
Title of host publication	2016 IEEE International Conference on Consumer Electronics, ICCE 2016
Editors	Francisco J. Bellido, Nicholas C. H. Vun, Carsten Dolar, Daniel Diaz-Sanchez, Wing-Kuen Ling
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	267-268
Number of pages	2
ISBN (Electronic)	9781467383646
DOIs	https://doi.org/10.1109/ICCE.2016.7430609
State	Published - 10 Mar 2016
Event	2016 IEEE International Conference on Consumer Electronics, ICCE 2016 - Las Vegas, United States Duration: 7 Jan 2016 → 11 Jan 2016

Publication series

Name	2016 IEEE International Conference on Consumer Electronics, ICCE 2016

Conference

Conference	2016 IEEE International Conference on Consumer Electronics, ICCE 2016
Country/Territory	United States
City	Las Vegas
Period	7/01/16 → 11/01/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 12 Responsible Consumption and Production

Access to Document

10.1109/ICCE.2016.7430609

Cite this

Lee, I., Jung, H., Ahn, C. H., Seo, J., Kim, J., & Kwon, O. (2016). Real-time personalized facial expression recognition system based on deep learning. In F. J. Bellido, N. C. H. Vun, C. Dolar, D. Diaz-Sanchez, & W.-K. Ling (Eds.), 2016 IEEE International Conference on Consumer Electronics, ICCE 2016 (pp. 267-268). Article 7430609 (2016 IEEE International Conference on Consumer Electronics, ICCE 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE.2016.7430609

Lee, Injae ; Jung, Heechul ; Ahn, Chung Hyun et al. / Real-time personalized facial expression recognition system based on deep learning. 2016 IEEE International Conference on Consumer Electronics, ICCE 2016. editor / Francisco J. Bellido ; Nicholas C. H. Vun ; Carsten Dolar ; Daniel Diaz-Sanchez ; Wing-Kuen Ling. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 267-268 (2016 IEEE International Conference on Consumer Electronics, ICCE 2016).

@inproceedings{1d5f7cad1b48488baa79aa065fcb8165,

title = "Real-time personalized facial expression recognition system based on deep learning",

abstract = "Over the last few years, deep learning has produced breakthrough results in many application fields including speech recognition, image understanding and so on. We try to deep learning techniques for real-time facial expression recognition instead of hand-crafted feature-based methods. The proposed system can recognize human emotions based on facial expressions using a webcam. It can detect faces and recognize users with a distance of 2∼3m for TV environment. And it can determine whether a user is feeling happiness, sadness, surprise, anger, disgust, neutral or any combination of those six emotions. The experimental results show that the proposed method achieves high accuracy. It can be used for various services such as consumer behavior research, usability studies, psychology, educational research, and market research.",

author = "Injae Lee and Heechul Jung and Ahn, \{Chung Hyun\} and Jeongil Seo and Junmo Kim and Ohseok Kwon",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Consumer Electronics, ICCE 2016 ; Conference date: 07-01-2016 Through 11-01-2016",

year = "2016",

month = mar,

day = "10",

doi = "10.1109/ICCE.2016.7430609",

language = "English",

series = "2016 IEEE International Conference on Consumer Electronics, ICCE 2016",

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

pages = "267--268",

editor = "Bellido, \{Francisco J.\} and Vun, \{Nicholas C. H.\} and Carsten Dolar and Daniel Diaz-Sanchez and Wing-Kuen Ling",

booktitle = "2016 IEEE International Conference on Consumer Electronics, ICCE 2016",

address = "United States",

}

Lee, I, Jung, H, Ahn, CH, Seo, J, Kim, J & Kwon, O 2016, Real-time personalized facial expression recognition system based on deep learning. in FJ Bellido, NCH Vun, C Dolar, D Diaz-Sanchez & W-K Ling (eds), 2016 IEEE International Conference on Consumer Electronics, ICCE 2016., 7430609, 2016 IEEE International Conference on Consumer Electronics, ICCE 2016, Institute of Electrical and Electronics Engineers Inc., pp. 267-268, 2016 IEEE International Conference on Consumer Electronics, ICCE 2016, Las Vegas, United States, 7/01/16. https://doi.org/10.1109/ICCE.2016.7430609

Real-time personalized facial expression recognition system based on deep learning. / Lee, Injae; Jung, Heechul; Ahn, Chung Hyun et al.
2016 IEEE International Conference on Consumer Electronics, ICCE 2016. ed. / Francisco J. Bellido; Nicholas C. H. Vun; Carsten Dolar; Daniel Diaz-Sanchez; Wing-Kuen Ling. Institute of Electrical and Electronics Engineers Inc., 2016. p. 267-268 7430609 (2016 IEEE International Conference on Consumer Electronics, ICCE 2016).

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

TY - GEN

T1 - Real-time personalized facial expression recognition system based on deep learning

AU - Lee, Injae

AU - Jung, Heechul

AU - Ahn, Chung Hyun

AU - Seo, Jeongil

AU - Kim, Junmo

AU - Kwon, Ohseok

PY - 2016/3/10

Y1 - 2016/3/10

N2 - Over the last few years, deep learning has produced breakthrough results in many application fields including speech recognition, image understanding and so on. We try to deep learning techniques for real-time facial expression recognition instead of hand-crafted feature-based methods. The proposed system can recognize human emotions based on facial expressions using a webcam. It can detect faces and recognize users with a distance of 2∼3m for TV environment. And it can determine whether a user is feeling happiness, sadness, surprise, anger, disgust, neutral or any combination of those six emotions. The experimental results show that the proposed method achieves high accuracy. It can be used for various services such as consumer behavior research, usability studies, psychology, educational research, and market research.

AB - Over the last few years, deep learning has produced breakthrough results in many application fields including speech recognition, image understanding and so on. We try to deep learning techniques for real-time facial expression recognition instead of hand-crafted feature-based methods. The proposed system can recognize human emotions based on facial expressions using a webcam. It can detect faces and recognize users with a distance of 2∼3m for TV environment. And it can determine whether a user is feeling happiness, sadness, surprise, anger, disgust, neutral or any combination of those six emotions. The experimental results show that the proposed method achieves high accuracy. It can be used for various services such as consumer behavior research, usability studies, psychology, educational research, and market research.

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M3 - Conference contribution

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BT - 2016 IEEE International Conference on Consumer Electronics, ICCE 2016

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PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Lee I, Jung H, Ahn CH, Seo J, Kim J, Kwon O. Real-time personalized facial expression recognition system based on deep learning. In Bellido FJ, Vun NCH, Dolar C, Diaz-Sanchez D, Ling WK, editors, 2016 IEEE International Conference on Consumer Electronics, ICCE 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 267-268. 7430609. (2016 IEEE International Conference on Consumer Electronics, ICCE 2016). doi: 10.1109/ICCE.2016.7430609

Real-time personalized facial expression recognition system based on deep learning

Abstract

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UN SDGs

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