Learning of facial gestures using SVMs

Jacky Baltes; Stela Seo; Chi Tai Cheng; M. C. Lau; John Anderson

doi:10.1007/978-3-642-23147-6_18

Learning of facial gestures using SVMs

Jacky Baltes^*, Stela Seo, Chi Tai Cheng, M. C. Lau, John Anderson

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper describes the implementation of a fast and accurate gesture recognition system. Image sequences are used to train a standard SVM to recognize Yes, No, and Neutral gestures from different users. We show that our system is able to detect facial gestures with more than 80% accuracy from even small input images.

Original language	English
Title of host publication	Next Wave in Robotics - 14th FIRA RoboWorld Congress, FIRA 2011, Proceedings
Pages	147-154
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-23147-6_18
Publication status	Published - 2011
Externally published	Yes
Event	14th FIRA RoboWorld Congress on Next Wave in Robotics, FIRA 2011 - Kaohsiung, Taiwan Duration: 2011 Aug 26 → 2011 Aug 30

Publication series

Name	Communications in Computer and Information Science
Volume	212 CCIS
ISSN (Print)	1865-0929

Other

Other	14th FIRA RoboWorld Congress on Next Wave in Robotics, FIRA 2011
Country/Territory	Taiwan
City	Kaohsiung
Period	2011/08/26 → 2011/08/30

Keywords

Facial Recognition
Machine Learning
SVM

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-3-642-23147-6_18

Cite this

Baltes, J, Seo, S, Cheng, CT, Lau, MC & Anderson, J 2011, Learning of facial gestures using SVMs. in Next Wave in Robotics - 14th FIRA RoboWorld Congress, FIRA 2011, Proceedings. Communications in Computer and Information Science, vol. 212 CCIS, pp. 147-154, 14th FIRA RoboWorld Congress on Next Wave in Robotics, FIRA 2011, Kaohsiung, Taiwan, 2011/08/26. https://doi.org/10.1007/978-3-642-23147-6_18

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title = "Learning of facial gestures using SVMs",

abstract = "This paper describes the implementation of a fast and accurate gesture recognition system. Image sequences are used to train a standard SVM to recognize Yes, No, and Neutral gestures from different users. We show that our system is able to detect facial gestures with more than 80% accuracy from even small input images.",

keywords = "Facial Recognition, Machine Learning, SVM",

author = "Jacky Baltes and Stela Seo and Cheng, {Chi Tai} and Lau, {M. C.} and John Anderson",

year = "2011",

doi = "10.1007/978-3-642-23147-6_18",

language = "English",

isbn = "9783642231469",

series = "Communications in Computer and Information Science",

pages = "147--154",

booktitle = "Next Wave in Robotics - 14th FIRA RoboWorld Congress, FIRA 2011, Proceedings",

note = "14th FIRA RoboWorld Congress on Next Wave in Robotics, FIRA 2011 ; Conference date: 26-08-2011 Through 30-08-2011",

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T1 - Learning of facial gestures using SVMs

AU - Baltes, Jacky

AU - Seo, Stela

AU - Cheng, Chi Tai

AU - Lau, M. C.

AU - Anderson, John

PY - 2011

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N2 - This paper describes the implementation of a fast and accurate gesture recognition system. Image sequences are used to train a standard SVM to recognize Yes, No, and Neutral gestures from different users. We show that our system is able to detect facial gestures with more than 80% accuracy from even small input images.

AB - This paper describes the implementation of a fast and accurate gesture recognition system. Image sequences are used to train a standard SVM to recognize Yes, No, and Neutral gestures from different users. We show that our system is able to detect facial gestures with more than 80% accuracy from even small input images.

KW - Facial Recognition

KW - Machine Learning

KW - SVM

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U2 - 10.1007/978-3-642-23147-6_18

DO - 10.1007/978-3-642-23147-6_18

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SN - 9783642231469

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BT - Next Wave in Robotics - 14th FIRA RoboWorld Congress, FIRA 2011, Proceedings

T2 - 14th FIRA RoboWorld Congress on Next Wave in Robotics, FIRA 2011

Y2 - 26 August 2011 through 30 August 2011

ER -

Learning of facial gestures using SVMs

Abstract

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Keywords

ASJC Scopus subject areas

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