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ICSES Transactions on Image Processing and Pattern Recognition
Vol. 4, No. 4, Dec. 2018
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Editors' Choice |
a Annamalai University, Chidambaram, India
b Annamalai University, Cuddalore, India
Mr. Santhoshkumar R
 orcid.org/0000-0002-3316-7638
 Corresponding Author
 Affiliation: Annamalai University, Chidambaram, India
 Tel: 7868933292
 E-mail: santhoshkumar.aucse@gmail.com
 Biography: R. Santhoshkumar Received his B.E. degree in Information Technology from Annamalai University in 2013 and M.E. degree in Computer Science and Engineering from Annamalai University, Annamalainagar, Tamilnadu. India in 2015. He is currently pursuing his Ph.D. in Computer Science and Engineering at Annamalai University India. He has published 8 research papers in international journals and presented 6 papers in national and international conferences. His current research interests include computer vision, video processing, pattern recognition, machine learning and deep learning.
Mr. Santhoshkumar R's publications in ICSES
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This article has been retracted by International Computer Science and Engineering Society (ICSES) because of ethical misconduct, scientific distortion, or administrative error, and cannot be downloaded and used for any purpose based on the violation in ICSES Ethics in Publicationcall_made |
Retraction Note by the Editor-in-Chief
Highlights and Novelties
1- To recognize emotion from human body movements and for static action sequence.
2- To identify the emotion and prevent the suspicious event from public places.
3- The Different Bin Level HoG (DBLHoG)feature perform better identification of emotion on GEMEP corpus dataset.
Manuscript Abstract
Automatic emotion recognition is becoming recent research focus today. A facet of human intelligence is the ability to recognize emotion that is regarded as one of the attribute of emotional intelligence. Although research based on facial expressions or speech is seen in thrive, recognizing emotions from body gestures has been remained as a less explored topic. This chapter proposes a machine learning approach to achieve emotional intelligence. A set of Different Bin Level HoG features (DBLHoG) and Spatio-Temporal Interest Points (STIP) are extracted from human body movements present in each frame and are fed to a supervised learning algorithm. This experiment is conducted by GEMEP corpus dataset. In this dataset human expressing the five archetypical emotions likes (anger, joy, sad, fear and pride) using body movements. In this emotions recognition problem, random forest classifier outperformed the kNN classifier by achieving an overall recognition accuracy of 94.8% for DBLHoG feature. Moreover, the performance can be measured by qualitative approach. Finally, this chapter gives a brief study on achieving emotional intelligence with a deep learning approach.
Keywords
Emotion Recognition Human body movements Histogram of Gradient (HoG) Random forest k Nearest Neighbor (kNN) Emotional intelligence Deep learning
Copyright and Licence
Copyright © International Computer Science and Engineering Society (ICSES). This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution Non Commercial 4.0 International (CC BY-NC 4.0) license, supported by creativecommons.orgcall_madeCite this manuscript as
Santhoshkumar R, Kalaiselvi Geetha M, "Deep Emotional Intelligence: Study on Discrete Action Sequences," in Image Segmentation: A Guide to Image Mining, 1st ed., ITIPPR: ICSES, 2018, pp. 83-94.
For External Scientific Databeses
--BibTex--
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title="Deep Emotional Intelligence: Study on Discrete Action Sequences",
author="Santhoshkumar R",
author="Kalaiselvi Geetha M",
journal="ICSES Transactions on Image Processing and Pattern Recognition (ITIPPR)",
volume="4",
number="4",
pages="83-94",
year="2018",
month="12",
day="30",
publisher= "International Computer Science and Engineering Society (ICSES)",
doi="",
url="http://www.i-cses.com/files/download.php?pID=225"}
--EndNote--
%0 Journal Article
%T Deep Emotional Intelligence: Study on Discrete Action Sequences
%A Santhoshkumar R
%A Kalaiselvi Geetha M
%J ICSES Transactions on Image Processing and Pattern Recognition (ITIPPR)
%V 4
%N 4
%P 83-94
%D 2018
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%8 2018-12-30
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%@ 2645-8071
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