Facial Expression Recognition Based on LBP Features and Entropy-regularized Wasserstein Distance
ZHENG Changjin
ZHANG Dengyi
SU Kehua
WU Xiaoping
HONG Cheng
Abstract：To solve quantification of similarity measure in the K-nearest neighbor classification, a KNN method is proposed based on LBP features and entropy-regularized Wasserstein distance, by combining the mathematical properties of Wasserstein distance in optimal mass transportation theory.Firstly, facial expression images are preprocessed.Secondly, LBP operator is applied to extract LBP feature histograms.Lastly, the K-nearest neighbor method with entropy-regularized Wasserstein distance as the similarity measure between feature histograms is used to recognize and classify facial expressions.Experimental results show that compared to the methods based on LBP only, the method greatly increases the recognition rate.
Keywords：optimal mass trassportationWasserstein distancehuman facial expression recognitionentropic regularizationK-nearnest neighbor classification
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 242-246,260 )