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Volume: 31 | Article ID: art00018
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Construction of facial emotion database through subjective experiments and its application to deep learning-based facial image processing
  DOI :  10.2352/ISSN.2470-1173.2019.11.IPAS-267  Published OnlineJanuary 2019
Abstract

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.

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  Cite this article 

Tomoyuki Takanashi, Keita Hirai, Takahiko Horiuchi, "Construction of facial emotion database through subjective experiments and its application to deep learning-based facial image processingin Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Processing: Algorithms and Systems XVII,  2019,  pp 267-1 - 267-7,  https://doi.org/10.2352/ISSN.2470-1173.2019.11.IPAS-267

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