Skip to main content
padlock icon - secure page this page is secure

Open Access Construction of facial emotion database through subjective experiments and its application to deep learning-based facial image processing

Download Article:
 Download
(PDF 1,867.3 kb)
 
As the development of interactive robots and machines, studies to understand and reproduce facial emotions by computers have become important research areas. For achieving this goal, several deep learning-based facial image analysis and synthesis techniques recently have been proposed. However, there are difficulties in the construction of facial image dataset having accurate emotion tags (annotations, metadata), because such emotion tags significantly depend on human perception and cognition. In this study, we constructed facial image dataset having accurate emotion tags through subjective experiments. First, based on image retrieval using the emotion terms, we collected more than 1,600,000 facial images from SNS. Next, based on a face detection image processing, we obtained approximately 380,000 facial region images as “big data.” Then, through subjective experiments, we manually checked the facial expression and the corresponding emotion tags of the facial regions. Finally, we achieved approximately 5,500 facial images having accurate emotion tags as “good data.” For validating our facial image dataset in deep learning-based facial image analysis and synthesis, we applied our dataset to CNN-based facial emotion recognition and GAN-based facial emotion reconstruction. Through these experiments, we confirmed the feasibility of our facial image dataset in deep learning-based emotion recognition and reconstruction.
No References for this article.
No Supplementary Data.
No Article Media
No Metrics

Keywords: accurate emotion tag; big data; convolutional neural network; facial emotion image; generative adversarial network; good data

Document Type: Research Article

Publication date: January 13, 2019

This article was made available online on January 13, 2019 as a Fast Track article with title: "Construction of facial emotion database through subjective experiments and its application to deep learning-based facial image processing".

More about this publication?
  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more