Facial emotion recognition using active shape models and statistical pattern recognizers

Gil Jin Jang; Jeong Sik Park; Ahra Jo; Ji Hwan Kim

doi:10.1109/BWCCA.2014.110

Facial emotion recognition using active shape models and statistical pattern recognizers

Gil Jin Jang, Jeong Sik Park, Ahra Jo, Ji Hwan Kim

School of Electronics Engineering

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

2 Scopus citations

Abstract

This paper investigates various emotion recognition techniques from the facial expression of human subjects. To describe human facial expressions, a number of characteristic points are extracted from input face images using active shape models (ASMs), and translated 49 scalar features so that they are invariant to scale and position changes. The scalar feature values then construct a 49-dimensional feature vector for each still image. Statistical pattern recognizers, such as support vector machine (SVM) and multi-layer perceptron (MLP), are used to identify various emotions from the feature vectors. To analyze the performances of the various pattern recognizers on the limited amount of image data, 5-fold cross-validation is carried out, with varying numbers of emotions from 3 to 6. Evaluation results show that SVM is the most stable and best in terms of emotion classification rates.

Original language	English
Title of host publication	Proceedings - 2014 9th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2014
Editors	Xiaofeng Chen, Makoto Ikeda, Leonard Barolli, Fatos Xhafa
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	514-517
Number of pages	4
ISBN (Electronic)	9781479941735
DOIs	https://doi.org/10.1109/BWCCA.2014.110
State	Published - 20 Jan 2014
Event	9th IEEE International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2014 - Guangzhou, Guangdong, China Duration: 8 Nov 2014 → 10 Nov 2014

Publication series

Name	Proceedings - 2014 9th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2014

Conference

Conference	9th IEEE International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2014
Country/Territory	China
City	Guangzhou, Guangdong
Period	8/11/14 → 10/11/14

Keywords

ASM (activa shape model)
cross validation
Emotion recognition
facial expression
pattern recognition
SVM (support vector machine)

Access to Document

10.1109/BWCCA.2014.110

Cite this

Jang, G. J., Park, J. S., Jo, A., & Kim, J. H. (2014). Facial emotion recognition using active shape models and statistical pattern recognizers. In X. Chen, M. Ikeda, L. Barolli, & F. Xhafa (Eds.), Proceedings - 2014 9th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2014 (pp. 514-517). Article 7016125 (Proceedings - 2014 9th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BWCCA.2014.110

Jang, Gil Jin ; Park, Jeong Sik ; Jo, Ahra et al. / Facial emotion recognition using active shape models and statistical pattern recognizers. Proceedings - 2014 9th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2014. editor / Xiaofeng Chen ; Makoto Ikeda ; Leonard Barolli ; Fatos Xhafa. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 514-517 (Proceedings - 2014 9th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2014).

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title = "Facial emotion recognition using active shape models and statistical pattern recognizers",

abstract = "This paper investigates various emotion recognition techniques from the facial expression of human subjects. To describe human facial expressions, a number of characteristic points are extracted from input face images using active shape models (ASMs), and translated 49 scalar features so that they are invariant to scale and position changes. The scalar feature values then construct a 49-dimensional feature vector for each still image. Statistical pattern recognizers, such as support vector machine (SVM) and multi-layer perceptron (MLP), are used to identify various emotions from the feature vectors. To analyze the performances of the various pattern recognizers on the limited amount of image data, 5-fold cross-validation is carried out, with varying numbers of emotions from 3 to 6. Evaluation results show that SVM is the most stable and best in terms of emotion classification rates.",

keywords = "ASM (activa shape model), cross validation, Emotion recognition, facial expression, pattern recognition, SVM (support vector machine)",

author = "Jang, {Gil Jin} and Park, {Jeong Sik} and Ahra Jo and Kim, {Ji Hwan}",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 9th IEEE International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2014 ; Conference date: 08-11-2014 Through 10-11-2014",

year = "2014",

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day = "20",

doi = "10.1109/BWCCA.2014.110",

language = "English",

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publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Jang, GJ, Park, JS, Jo, A & Kim, JH 2014, Facial emotion recognition using active shape models and statistical pattern recognizers. in X Chen, M Ikeda, L Barolli & F Xhafa (eds), Proceedings - 2014 9th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2014., 7016125, Proceedings - 2014 9th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2014, Institute of Electrical and Electronics Engineers Inc., pp. 514-517, 9th IEEE International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2014, Guangzhou, Guangdong, China, 8/11/14. https://doi.org/10.1109/BWCCA.2014.110

Facial emotion recognition using active shape models and statistical pattern recognizers. / Jang, Gil Jin; Park, Jeong Sik; Jo, Ahra et al.
Proceedings - 2014 9th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2014. ed. / Xiaofeng Chen; Makoto Ikeda; Leonard Barolli; Fatos Xhafa. Institute of Electrical and Electronics Engineers Inc., 2014. p. 514-517 7016125 (Proceedings - 2014 9th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2014).

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

TY - GEN

T1 - Facial emotion recognition using active shape models and statistical pattern recognizers

AU - Jang, Gil Jin

AU - Park, Jeong Sik

AU - Jo, Ahra

AU - Kim, Ji Hwan

PY - 2014/1/20

Y1 - 2014/1/20

N2 - This paper investigates various emotion recognition techniques from the facial expression of human subjects. To describe human facial expressions, a number of characteristic points are extracted from input face images using active shape models (ASMs), and translated 49 scalar features so that they are invariant to scale and position changes. The scalar feature values then construct a 49-dimensional feature vector for each still image. Statistical pattern recognizers, such as support vector machine (SVM) and multi-layer perceptron (MLP), are used to identify various emotions from the feature vectors. To analyze the performances of the various pattern recognizers on the limited amount of image data, 5-fold cross-validation is carried out, with varying numbers of emotions from 3 to 6. Evaluation results show that SVM is the most stable and best in terms of emotion classification rates.

AB - This paper investigates various emotion recognition techniques from the facial expression of human subjects. To describe human facial expressions, a number of characteristic points are extracted from input face images using active shape models (ASMs), and translated 49 scalar features so that they are invariant to scale and position changes. The scalar feature values then construct a 49-dimensional feature vector for each still image. Statistical pattern recognizers, such as support vector machine (SVM) and multi-layer perceptron (MLP), are used to identify various emotions from the feature vectors. To analyze the performances of the various pattern recognizers on the limited amount of image data, 5-fold cross-validation is carried out, with varying numbers of emotions from 3 to 6. Evaluation results show that SVM is the most stable and best in terms of emotion classification rates.

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KW - cross validation

KW - Emotion recognition

KW - facial expression

KW - pattern recognition

KW - SVM (support vector machine)

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T3 - Proceedings - 2014 9th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2014

SP - 514

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BT - Proceedings - 2014 9th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2014

A2 - Chen, Xiaofeng

A2 - Ikeda, Makoto

A2 - Barolli, Leonard

A2 - Xhafa, Fatos

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 8 November 2014 through 10 November 2014

ER -

Jang GJ, Park JS, Jo A, Kim JH. Facial emotion recognition using active shape models and statistical pattern recognizers. In Chen X, Ikeda M, Barolli L, Xhafa F, editors, Proceedings - 2014 9th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 514-517. 7016125. (Proceedings - 2014 9th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2014). doi: 10.1109/BWCCA.2014.110

Facial emotion recognition using active shape models and statistical pattern recognizers

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Keywords

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