Paper Info
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.
Year
DOI
Venue
2016
10.1109/RCAR.2016.7784056
2016 IEEE International Conference on Real-time Computing and Robotics (RCAR)
Keywords
Field
DocType
facial expression recognition,PCA,LBP feature extraction,active facial patches,natural user interface,NUI,gray pixel value,local binary patterns,principal component analysis,softmax regression classifier,leave-one-out cross validation method
Computer vision,Expression (mathematics),Pattern recognition,Softmax function,Computer science,Local binary patterns,Feature extraction,Facial expression,Artificial intelligence,Classifier (linguistics),Cross-validation,Principal component analysis
Conference
ISBN
Citations 
PageRank 
978-1-4673-8960-0
0
0.34
References 
Authors
10
8
Name
Order
Citations
PageRank
Yanpeng Liu100.34
Yuwen Cao200.34
Yibin Li322659.56
Ming Liu477594.83
Rui Song501.35
Yafang Wang613413.56
Zhigang Xu7143.17
Xin Ma88917.25