YongHwan Lee

Prof. YongHwan Lee

orcid

Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 December 2015 in Pervasive and Mobile Computing
Verified
Journal Article
Closed access
RHRasheed Hussain
Rasheed Hussain
ZRZeinab Rezaeifar
Zeinab Rezaeifar
YLYongHwan Lee
YongHwan Lee
HOHeekuck Oh
Heekuck Oh

21 May 2018 in Journal of Ambient Intelligence and Humanized Computing
Verified
Journal Article
Closed access
PDF file
YLYongHwan Lee
YongHwan Lee
SBSung-Il Bang
Sung-Il Bang

2 July 2014 in Multimedia Tools and Applications
Verified
Journal Article
Closed access
PDF file
YLYongHwan Lee
YongHwan Lee
YKYoungseop Kim
Youngseop Kim

30 June 2013 in Multimedia Tools and Applications
Verified
Journal Article
Closed access
PDF file
YLYongHwan Lee
YongHwan Lee
CKCheong Kim
Cheong Kim
YKYoungseop Kim
Youngseop Kim
TWTaegkeun Whangbo
Taegkeun Whangbo

6 October 2015 in Journal of Ambient Intelligence and Humanized Computing
Verified
Journal Article
Closed access
PDF file
HAHyochang Ahn
Hyochang Ahn
YLYongHwan Lee
YongHwan Lee

16 December 2015 in Indian Journal of Science and Technology
Verified
Journal Article
Open access
PDF file
YLYongHwan Lee
YongHwan Lee
HAHyochang Ahn
Hyochang Ahn
HCHan-Jin Cho
Han-Jin Cho
JLJune-Hwan Lee
June-Hwan Lee
Emotion recognition is a challenging task of human-computer interface in wireless communication. Emotion recognition from speech has a problem with the quality of the input voice, which is difficult to ensure in the mobile environment. On the contrary, facial emotion recognition is one of the interesting subjects due to the relevance of the expressions on human emotions. This paper proposes an automatic extraction and interpretation method of facial expression using extraction of feature points and variation of the Bezier curve from still image. The proposed algorithm has three steps to recognize the facial emotion: (1) Detecting facial regions with feature map, (2) Drawing the Bezier curve on eye and mouth, and (3) Classifying the emotion of characteristic with Hausdorff distance. To evaluate the proposed recognition scheme, we estimate a success-ratio with emotionally expressive facial image repository. Experimental results show average 76.1% of success to interpret and classify the facial expression and emotion.