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Facial expression recognition based on improved local binary pattern and class-regularized locality preserving projection
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DOI：10.1016/j.sigpro.2015.04.007.png)
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This paper provides a novel method for facial expression recognition, which distinguishes itself with the following two main contributions. First, an improved facial feature, called the expression-specific local binary pattern (es-LBP), is presented by emphasizing the partial information of human faces on particular fiducial points. Second, to enhance the connection between facial features and expression classes, class-regularized locality preserving projection (cr-LPP) is proposed, which aims at maximizing the class independence and simultaneously preserving the local feature similarity via dimensionality reduction. Simulation results show that the proposed approach is very effective for facial expression recognition. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Facial expression
Expression-specific local binary pattern
Class-regularized locality preserving projection
Dimensionality reduction
Feature extraction
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