Features representation by multiple local binary patterns for facial expression recognition

Li Wang; Ruifeng Li; Ke Wang

doi:10.1109/WCICA.2014.7053274

Features representation by multiple local binary patterns for facial expression recognition

Li Wang, Ruifeng Li, Ke Wang

Harbin Institute of Technology

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

Abstract

To recognize expressions conveniently and effectively, an enhanced feature representation method is proposed for facial expression recognition. Local binary pattern histogram Fourier (HF-LBP) features is used to represent facial expression features. Multiple HF-LBP features are extracted to form recognition vectors for facial expression recognition in the approach, which include sign and magnitude LBP in the completed LBP scheme with multiple radii and different size neighborhoods to achieve enough features. It represents images from different scales and directions in the local neighborhood by overall considerations from the aspect. K-nearest neighborhoods classifier is applied for expression recognition after representing facial features using HF-MLBP. Comparisons are made with other extension LBP operators to evaluate the approach. The experimental results show that our method has good performance in facial expression recognition.

Original language	English
Title of host publication	Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3369-3374
Number of pages	6
Edition	March
ISBN (Electronic)	9781479958252
DOIs	https://doi.org/10.1109/WCICA.2014.7053274
Publication status	Published - 2 Mar 2015
Externally published	Yes
Event	2014 11th World Congress on Intelligent Control and Automation, WCICA 2014 - Shenyang, China Duration: 29 Jun 2014 → 4 Jul 2014

Publication series

Name	Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
Number	March
Volume	2015-March

Conference

Conference	2014 11th World Congress on Intelligent Control and Automation, WCICA 2014
Country/Territory	China
City	Shenyang
Period	29/06/14 → 4/07/14

Keywords

Facial expression recognition
Feature Fourier transform
Local binary patterns
Multiple features

Access to Document

10.1109/WCICA.2014.7053274

Cite this

Wang, L., Li, R., & Wang, K. (2015). Features representation by multiple local binary patterns for facial expression recognition. In Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014 (March ed., pp. 3369-3374). Article 7053274 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); Vol. 2015-March, No. March). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WCICA.2014.7053274

Wang, Li ; Li, Ruifeng ; Wang, Ke. / Features representation by multiple local binary patterns for facial expression recognition. Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014. March. ed. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 3369-3374 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); March).

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title = "Features representation by multiple local binary patterns for facial expression recognition",

abstract = "To recognize expressions conveniently and effectively, an enhanced feature representation method is proposed for facial expression recognition. Local binary pattern histogram Fourier (HF-LBP) features is used to represent facial expression features. Multiple HF-LBP features are extracted to form recognition vectors for facial expression recognition in the approach, which include sign and magnitude LBP in the completed LBP scheme with multiple radii and different size neighborhoods to achieve enough features. It represents images from different scales and directions in the local neighborhood by overall considerations from the aspect. K-nearest neighborhoods classifier is applied for expression recognition after representing facial features using HF-MLBP. Comparisons are made with other extension LBP operators to evaluate the approach. The experimental results show that our method has good performance in facial expression recognition.",

keywords = "Facial expression recognition, Feature Fourier transform, Local binary patterns, Multiple features",

author = "Li Wang and Ruifeng Li and Ke Wang",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 11th World Congress on Intelligent Control and Automation, WCICA 2014 ; Conference date: 29-06-2014 Through 04-07-2014",

year = "2015",

month = mar,

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doi = "10.1109/WCICA.2014.7053274",

language = "English",

series = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",

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

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Wang, L, Li, R & Wang, K 2015, Features representation by multiple local binary patterns for facial expression recognition. in Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014. March edn, 7053274, Proceedings of the World Congress on Intelligent Control and Automation (WCICA), no. March, vol. 2015-March, Institute of Electrical and Electronics Engineers Inc., pp. 3369-3374, 2014 11th World Congress on Intelligent Control and Automation, WCICA 2014, Shenyang, China, 29/06/14. https://doi.org/10.1109/WCICA.2014.7053274

Features representation by multiple local binary patterns for facial expression recognition. / Wang, Li; Li, Ruifeng; Wang, Ke.
Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014. March. ed. Institute of Electrical and Electronics Engineers Inc., 2015. p. 3369-3374 7053274 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); Vol. 2015-March, No. March).

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

TY - GEN

T1 - Features representation by multiple local binary patterns for facial expression recognition

AU - Wang, Li

AU - Li, Ruifeng

AU - Wang, Ke

PY - 2015/3/2

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N2 - To recognize expressions conveniently and effectively, an enhanced feature representation method is proposed for facial expression recognition. Local binary pattern histogram Fourier (HF-LBP) features is used to represent facial expression features. Multiple HF-LBP features are extracted to form recognition vectors for facial expression recognition in the approach, which include sign and magnitude LBP in the completed LBP scheme with multiple radii and different size neighborhoods to achieve enough features. It represents images from different scales and directions in the local neighborhood by overall considerations from the aspect. K-nearest neighborhoods classifier is applied for expression recognition after representing facial features using HF-MLBP. Comparisons are made with other extension LBP operators to evaluate the approach. The experimental results show that our method has good performance in facial expression recognition.

AB - To recognize expressions conveniently and effectively, an enhanced feature representation method is proposed for facial expression recognition. Local binary pattern histogram Fourier (HF-LBP) features is used to represent facial expression features. Multiple HF-LBP features are extracted to form recognition vectors for facial expression recognition in the approach, which include sign and magnitude LBP in the completed LBP scheme with multiple radii and different size neighborhoods to achieve enough features. It represents images from different scales and directions in the local neighborhood by overall considerations from the aspect. K-nearest neighborhoods classifier is applied for expression recognition after representing facial features using HF-MLBP. Comparisons are made with other extension LBP operators to evaluate the approach. The experimental results show that our method has good performance in facial expression recognition.

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KW - Feature Fourier transform

KW - Local binary patterns

KW - Multiple features

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Wang L, Li R, Wang K. Features representation by multiple local binary patterns for facial expression recognition. In Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014. March ed. Institute of Electrical and Electronics Engineers Inc. 2015. p. 3369-3374. 7053274. (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); March). doi: 10.1109/WCICA.2014.7053274

Features representation by multiple local binary patterns for facial expression recognition

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