Improving performance of facial expression recognition using multi-task learning of neural networks

Jeongin Seo; Changhun Hyun; Hyeyoung Park

doi:10.1145/2814940.2815011

Improving performance of facial expression recognition using multi-task learning of neural networks

Jeongin Seo, Changhun Hyun, Hyeyoung Park

Kyungpook National University

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

1 Scopus citations

Abstract

Facial expression recognition is an important topic in the field of human-agent interaction, because facial expression is simple and impressive signal which human can send to others. Though there have been numerous studies on facial image analysis, the performance of expression recognition is still not acceptable due to the diversity of human expression and enormous variations in facial images. In this paper, we try to improve the performance of facial expression recognition by using multi-task learning techniques of neural networks. Through computational experiments on a benchmark database, we show positive possibility of performance improvement using multi-task learning.

Original language	English
Title of host publication	HAI 2015 - Proceedings of the 3rd International Conference on Human-Agent Interaction
Publisher	Association for Computing Machinery, Inc
Pages	327-328
Number of pages	2
ISBN (Electronic)	9781450335270
DOIs	https://doi.org/10.1145/2814940.2815011
State	Published - 21 Oct 2015
Event	3rd International Conference on Human-Agent Interaction, HAI 2015 - Daegu, Korea, Republic of Duration: 21 Oct 2015 → 24 Oct 2015

Publication series

Name	HAI 2015 - Proceedings of the 3rd International Conference on Human-Agent Interaction

Conference

Conference	3rd International Conference on Human-Agent Interaction, HAI 2015
Country/Territory	Korea, Republic of
City	Daegu
Period	21/10/15 → 24/10/15

Keywords

Facial expression recognition
Machine learning
Multitask learning
Neural networks

Access to Document

10.1145/2814940.2815011

Cite this

Seo, J., Hyun, C., & Park, H. (2015). Improving performance of facial expression recognition using multi-task learning of neural networks. In HAI 2015 - Proceedings of the 3rd International Conference on Human-Agent Interaction (pp. 327-328). (HAI 2015 - Proceedings of the 3rd International Conference on Human-Agent Interaction). Association for Computing Machinery, Inc. https://doi.org/10.1145/2814940.2815011

Seo, Jeongin ; Hyun, Changhun ; Park, Hyeyoung. / Improving performance of facial expression recognition using multi-task learning of neural networks. HAI 2015 - Proceedings of the 3rd International Conference on Human-Agent Interaction. Association for Computing Machinery, Inc, 2015. pp. 327-328 (HAI 2015 - Proceedings of the 3rd International Conference on Human-Agent Interaction).

@inproceedings{7551755e97bc435b86a72841ebfa7572,

title = "Improving performance of facial expression recognition using multi-task learning of neural networks",

abstract = "Facial expression recognition is an important topic in the field of human-agent interaction, because facial expression is simple and impressive signal which human can send to others. Though there have been numerous studies on facial image analysis, the performance of expression recognition is still not acceptable due to the diversity of human expression and enormous variations in facial images. In this paper, we try to improve the performance of facial expression recognition by using multi-task learning techniques of neural networks. Through computational experiments on a benchmark database, we show positive possibility of performance improvement using multi-task learning.",

keywords = "Facial expression recognition, Machine learning, Multitask learning, Neural networks",

author = "Jeongin Seo and Changhun Hyun and Hyeyoung Park",

note = "Publisher Copyright: {\textcopyright} 2015 ACM.; 3rd International Conference on Human-Agent Interaction, HAI 2015 ; Conference date: 21-10-2015 Through 24-10-2015",

year = "2015",

month = oct,

day = "21",

doi = "10.1145/2814940.2815011",

language = "English",

series = "HAI 2015 - Proceedings of the 3rd International Conference on Human-Agent Interaction",

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Seo, J, Hyun, C & Park, H 2015, Improving performance of facial expression recognition using multi-task learning of neural networks. in HAI 2015 - Proceedings of the 3rd International Conference on Human-Agent Interaction. HAI 2015 - Proceedings of the 3rd International Conference on Human-Agent Interaction, Association for Computing Machinery, Inc, pp. 327-328, 3rd International Conference on Human-Agent Interaction, HAI 2015, Daegu, Korea, Republic of, 21/10/15. https://doi.org/10.1145/2814940.2815011

Improving performance of facial expression recognition using multi-task learning of neural networks. / Seo, Jeongin; Hyun, Changhun; Park, Hyeyoung.
HAI 2015 - Proceedings of the 3rd International Conference on Human-Agent Interaction. Association for Computing Machinery, Inc, 2015. p. 327-328 (HAI 2015 - Proceedings of the 3rd International Conference on Human-Agent Interaction).

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

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N2 - Facial expression recognition is an important topic in the field of human-agent interaction, because facial expression is simple and impressive signal which human can send to others. Though there have been numerous studies on facial image analysis, the performance of expression recognition is still not acceptable due to the diversity of human expression and enormous variations in facial images. In this paper, we try to improve the performance of facial expression recognition by using multi-task learning techniques of neural networks. Through computational experiments on a benchmark database, we show positive possibility of performance improvement using multi-task learning.

AB - Facial expression recognition is an important topic in the field of human-agent interaction, because facial expression is simple and impressive signal which human can send to others. Though there have been numerous studies on facial image analysis, the performance of expression recognition is still not acceptable due to the diversity of human expression and enormous variations in facial images. In this paper, we try to improve the performance of facial expression recognition by using multi-task learning techniques of neural networks. Through computational experiments on a benchmark database, we show positive possibility of performance improvement using multi-task learning.

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M3 - Conference contribution

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Seo J, Hyun C, Park H. Improving performance of facial expression recognition using multi-task learning of neural networks. In HAI 2015 - Proceedings of the 3rd International Conference on Human-Agent Interaction. Association for Computing Machinery, Inc. 2015. p. 327-328. (HAI 2015 - Proceedings of the 3rd International Conference on Human-Agent Interaction). doi: 10.1145/2814940.2815011

Improving performance of facial expression recognition using multi-task learning of neural networks

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