Facial expression recognition with PCA and LBP features extracting from active facial patches

Yanpeng Liu; Yuwen Cao; Yibin Li; Ming Liu; Rui Song; Yafang Wang; Zhigang Xu; Xin Ma

doi:10.1109/RCAR.2016.7784056

Facial expression recognition with PCA and LBP features extracting from active facial patches

Yanpeng Liu, Yuwen Cao, Yibin Li, Ming Liu, Rui Song, Yafang Wang, Zhigang Xu, Xin Ma^*

^*Corresponding author for this work

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

33 Citations (Scopus)

Abstract

Facial expression recognition is an important part of Natural User Interface (NUI). Feature extraction is one important step which could contribute to fast and accurate expression recognition. In order to extract more effective features from the static images, this paper proposes an algorithm based on the combination of gray pixel value and Local Binary Patterns (LBP) features. Principal component analysis (PCA) is used to reduce dimensions of the features which are combined by the gray pixel value and Local Binary Patterns (LBP) features. All the features are extracted from the active facial patches. The active facial patches are these face regions which undergo a major change during different expressions. Softmax regression classifier is used to classify the six basic facial expressions, the experimental results on extended Cohn-Kanade (CK+) database gain an average recognition rate of 96.3% under leave-one-out cross validation method which validates every subject in the database.

Original language	English
Title of host publication	2016 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	368-373
Number of pages	6
ISBN (Electronic)	9781467389594
DOIs	https://doi.org/10.1109/RCAR.2016.7784056
Publication status	Published - 14 Dec 2016
Externally published	Yes
Event	2016 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2016 - Siem Reap, Cambodia Duration: 6 Jun 2016 → 9 Jun 2016

Publication series

Name	2016 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2016

Conference

Conference	2016 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2016
Country/Territory	Cambodia
City	Siem Reap
Period	6/06/16 → 9/06/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Access to Document

10.1109/RCAR.2016.7784056

Cite this

Liu, Y., Cao, Y., Li, Y., Liu, M., Song, R., Wang, Y., Xu, Z., & Ma, X. (2016). Facial expression recognition with PCA and LBP features extracting from active facial patches. In 2016 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2016 (pp. 368-373). Article 7784056 (2016 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RCAR.2016.7784056

Liu, Yanpeng ; Cao, Yuwen ; Li, Yibin et al. / Facial expression recognition with PCA and LBP features extracting from active facial patches. 2016 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 368-373 (2016 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2016).

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title = "Facial expression recognition with PCA and LBP features extracting from active facial patches",

abstract = "Facial expression recognition is an important part of Natural User Interface (NUI). Feature extraction is one important step which could contribute to fast and accurate expression recognition. In order to extract more effective features from the static images, this paper proposes an algorithm based on the combination of gray pixel value and Local Binary Patterns (LBP) features. Principal component analysis (PCA) is used to reduce dimensions of the features which are combined by the gray pixel value and Local Binary Patterns (LBP) features. All the features are extracted from the active facial patches. The active facial patches are these face regions which undergo a major change during different expressions. Softmax regression classifier is used to classify the six basic facial expressions, the experimental results on extended Cohn-Kanade (CK+) database gain an average recognition rate of 96.3\% under leave-one-out cross validation method which validates every subject in the database.",

author = "Yanpeng Liu and Yuwen Cao and Yibin Li and Ming Liu and Rui Song and Yafang Wang and Zhigang Xu and Xin Ma",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2016 ; Conference date: 06-06-2016 Through 09-06-2016",

year = "2016",

month = dec,

day = "14",

doi = "10.1109/RCAR.2016.7784056",

language = "English",

series = "2016 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2016",

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Liu, Y, Cao, Y, Li, Y, Liu, M, Song, R, Wang, Y, Xu, Z & Ma, X 2016, Facial expression recognition with PCA and LBP features extracting from active facial patches. in 2016 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2016., 7784056, 2016 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2016, Institute of Electrical and Electronics Engineers Inc., pp. 368-373, 2016 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2016, Siem Reap, Cambodia, 6/06/16. https://doi.org/10.1109/RCAR.2016.7784056

Facial expression recognition with PCA and LBP features extracting from active facial patches. / Liu, Yanpeng; Cao, Yuwen; Li, Yibin et al.
2016 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 368-373 7784056 (2016 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2016).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

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T1 - Facial expression recognition with PCA and LBP features extracting from active facial patches

AU - Liu, Yanpeng

AU - Cao, Yuwen

AU - Li, Yibin

AU - Liu, Ming

AU - Song, Rui

AU - Wang, Yafang

AU - Xu, Zhigang

AU - Ma, Xin

PY - 2016/12/14

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N2 - Facial expression recognition is an important part of Natural User Interface (NUI). Feature extraction is one important step which could contribute to fast and accurate expression recognition. In order to extract more effective features from the static images, this paper proposes an algorithm based on the combination of gray pixel value and Local Binary Patterns (LBP) features. Principal component analysis (PCA) is used to reduce dimensions of the features which are combined by the gray pixel value and Local Binary Patterns (LBP) features. All the features are extracted from the active facial patches. The active facial patches are these face regions which undergo a major change during different expressions. Softmax regression classifier is used to classify the six basic facial expressions, the experimental results on extended Cohn-Kanade (CK+) database gain an average recognition rate of 96.3% under leave-one-out cross validation method which validates every subject in the database.

AB - Facial expression recognition is an important part of Natural User Interface (NUI). Feature extraction is one important step which could contribute to fast and accurate expression recognition. In order to extract more effective features from the static images, this paper proposes an algorithm based on the combination of gray pixel value and Local Binary Patterns (LBP) features. Principal component analysis (PCA) is used to reduce dimensions of the features which are combined by the gray pixel value and Local Binary Patterns (LBP) features. All the features are extracted from the active facial patches. The active facial patches are these face regions which undergo a major change during different expressions. Softmax regression classifier is used to classify the six basic facial expressions, the experimental results on extended Cohn-Kanade (CK+) database gain an average recognition rate of 96.3% under leave-one-out cross validation method which validates every subject in the database.

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Liu Y, Cao Y, Li Y, Liu M, Song R, Wang Y et al. Facial expression recognition with PCA and LBP features extracting from active facial patches. In 2016 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 368-373. 7784056. (2016 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2016). doi: 10.1109/RCAR.2016.7784056

Facial expression recognition with PCA and LBP features extracting from active facial patches

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