research-article

Improving Performance of Facial Expression Recognition using Multi-task Learning of Neural Networks

Authors:

Jeongin Seo,

Changhun Hyun,

Hyeyoung ParkAuthors Info & Claims

HAI '15: Proceedings of the 3rd International Conference on Human-Agent Interaction

Pages 327 - 328

https://doi.org/10.1145/2814940.2815011

Published: 21 October 2015 Publication History

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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.

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Cited By

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Hyun CPark H(2017)Recognition of Facial Attributes Using Multi-Task Learning of Deep NetworksProceedings of the 9th International Conference on Machine Learning and Computing10.1145/3055635.3056618(284-288)Online publication date: 24-Feb-2017
https://dl.acm.org/doi/10.1145/3055635.3056618

Index Terms

Improving Performance of Facial Expression Recognition using Multi-task Learning of Neural Networks
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Neural networks
2. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

HAI '15: Proceedings of the 3rd International Conference on Human-Agent Interaction

October 2015

254 pages

ISBN:9781450335270

DOI:10.1145/2814940

General Chairs:
Minho Lee
Kyungpook National University, Korea
,
Takashi Omori
Tamagawa University, Japan
,
Program Chairs:
Hirotaka Osawa
University of Tsukuba, Japan
,
Hyeyoung Park
Kyungpook National University, Korea
,
James Young
University of Manitoba, Canada

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

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Published: 21 October 2015

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BESK

HAI 2015: The Third International Conference on Human-Agent Interaction

October 21 - 24, 2015

Kyungpook, Daegu, Republic of Korea

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Hyun CPark H(2017)Recognition of Facial Attributes Using Multi-Task Learning of Deep NetworksProceedings of the 9th International Conference on Machine Learning and Computing10.1145/3055635.3056618(284-288)Online publication date: 24-Feb-2017
https://dl.acm.org/doi/10.1145/3055635.3056618

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Facial expression recognition with Convolutional Neural Networks

Expression-invariant face recognition by facial expression transformations

Facial expression recognition using dual dictionary learning

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