Dynamic texture and geometry features for facial expression recognition in video

Junkai Chen; Zenghai Chen; Zheru Chi; Hong Fu

doi:10.1109/ICIP.2015.7351752

Dynamic texture and geometry features for facial expression recognition in video

Junkai Chen
, Zenghai Chen
, Zheru Chi
, Hong Fu

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

18 Citations (Scopus)

Abstract

Facial expression recognition in video has attracted growing attention recently. In this paper, we propose to handle this problem with dynamic appearance and geometric features. We propose a new feature descriptor called HOG from Three Orthogonal Planes (HOG-TOP) to represent dynamic features. In addition, we introduce two types of geometry features to represent the facial rigid changes and non-rigid changes, respectively. Multiple Kernel Learning (MKL) is applied to find an optimal combination of two types of features. And finally a Support Vector Machine (SVM) with multiple kernels is trained for the facial expression classification. Extensive experiments conducted on the extended Cohn-Kanade dataset show that our method can achieve a competitive performance compared with the other state-of-the-art methods.

Original language	English
Title of host publication	2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
Publisher	IEEE Computer Society
Pages	4967-4971
Number of pages	5
Volume	2015-December
ISBN (Electronic)	9781479983391
DOIs	https://doi.org/10.1109/ICIP.2015.7351752
Publication status	Published - 9 Dec 2015
Event	IEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada Duration: 27 Sept 2015 → 30 Sept 2015

Conference

Conference	IEEE International Conference on Image Processing, ICIP 2015
Country/Territory	Canada
City	Quebec City
Period	27/09/15 → 30/09/15

Keywords

Facial expression recognition
Geometry features
Multiple Kernel Learning
texture

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

More information

10.1109/ICIP.2015.7351752

Cite this

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title = "Dynamic texture and geometry features for facial expression recognition in video",

abstract = "Facial expression recognition in video has attracted growing attention recently. In this paper, we propose to handle this problem with dynamic appearance and geometric features. We propose a new feature descriptor called HOG from Three Orthogonal Planes (HOG-TOP) to represent dynamic features. In addition, we introduce two types of geometry features to represent the facial rigid changes and non-rigid changes, respectively. Multiple Kernel Learning (MKL) is applied to find an optimal combination of two types of features. And finally a Support Vector Machine (SVM) with multiple kernels is trained for the facial expression classification. Extensive experiments conducted on the extended Cohn-Kanade dataset show that our method can achieve a competitive performance compared with the other state-of-the-art methods.",

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Chen, J, Chen, Z, Chi, Z & Fu, H 2015, Dynamic texture and geometry features for facial expression recognition in video. in 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings. vol. 2015-December, 7351752, IEEE Computer Society, pp. 4967-4971, IEEE International Conference on Image Processing, ICIP 2015, Quebec City, Canada, 27/09/15. https://doi.org/10.1109/ICIP.2015.7351752

Dynamic texture and geometry features for facial expression recognition in video. / Chen, Junkai; Chen, Zenghai; Chi, Zheru et al.
2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings. Vol. 2015-December IEEE Computer Society, 2015. p. 4967-4971 7351752.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

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AU - Chen, Zenghai

AU - Chi, Zheru

AU - Fu, Hong

PY - 2015/12/9

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N2 - Facial expression recognition in video has attracted growing attention recently. In this paper, we propose to handle this problem with dynamic appearance and geometric features. We propose a new feature descriptor called HOG from Three Orthogonal Planes (HOG-TOP) to represent dynamic features. In addition, we introduce two types of geometry features to represent the facial rigid changes and non-rigid changes, respectively. Multiple Kernel Learning (MKL) is applied to find an optimal combination of two types of features. And finally a Support Vector Machine (SVM) with multiple kernels is trained for the facial expression classification. Extensive experiments conducted on the extended Cohn-Kanade dataset show that our method can achieve a competitive performance compared with the other state-of-the-art methods.

AB - Facial expression recognition in video has attracted growing attention recently. In this paper, we propose to handle this problem with dynamic appearance and geometric features. We propose a new feature descriptor called HOG from Three Orthogonal Planes (HOG-TOP) to represent dynamic features. In addition, we introduce two types of geometry features to represent the facial rigid changes and non-rigid changes, respectively. Multiple Kernel Learning (MKL) is applied to find an optimal combination of two types of features. And finally a Support Vector Machine (SVM) with multiple kernels is trained for the facial expression classification. Extensive experiments conducted on the extended Cohn-Kanade dataset show that our method can achieve a competitive performance compared with the other state-of-the-art methods.

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KW - Geometry features

KW - Multiple Kernel Learning

KW - texture

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DO - 10.1109/ICIP.2015.7351752

M3 - Conference article published in proceeding or book

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BT - 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings

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T2 - IEEE International Conference on Image Processing, ICIP 2015

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ER -

Dynamic texture and geometry features for facial expression recognition in video

Abstract

Conference

Keywords

ASJC Scopus subject areas

More information

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