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공공누리This item is licensed Korea Open Government License

Title
Facial Data Visualization for Improved Deep Learning Based Emotion Recognition
Author(s)
Lee, Seung Ho
Publisher
Korea Institute of Science and Technology Information
Publication Year
2019-06-30
Abstract
A convolutional neural network (CNN) has been widely used in facial expression recognition (FER) because it can automatically learn discriminative appearance features from an expression image. To make full use of its discriminating capability, this paper suggests a simple but effective method for CNN based FER. Specifically, instead of an original expression image that contains facial appearance only, the expression image with facial geometry visualization is used as input to CNN. In this way, geometric and appearance features could be simultaneously learned, making CNN more discriminative for FER. A simple CNN extension is also presented in this paper, aiming to utilize geometric expression change derived from an expression image sequence. Experimental results on two public datasets (CK+ and MMI) show that CNN using facial geometry visualization clearly outperforms the conventional CNN using facial appearance only.
Keyword
facial expression recognition; convolutional neural network; facial landmark points; facial geometry visualization
Journal Title
Journal of Information Science Theory and Practice;Volume 7 Issue 2
ISSN
2287-4577
DOI
10.1633/JISTaP.2019.7.2.3
Files in This Item:
Thumbnail Lee_v7n2_32-39.pdf733.89 kBDownload
Appears in Collections:
8. KISTI 간행물 > JISTaP > Vol. 7 - No. 2
Type
Article
URI
https://repository.kisti.re.kr/handle/10580/13510
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