Facial expression recognition based on local binary patterns and kernel discriminant isomap

Sensors
Xiaoming Zhao, Shiqing Zhang

Abstract

Facial expression recognition is an interesting and challenging subject. Considering the nonlinear manifold structure of facial images, a new kernel-based manifold learning method, called kernel discriminant isometric mapping (KDIsomap), is proposed. KDIsomap aims to nonlinearly extract the discriminant information by maximizing the interclass scatter while minimizing the intraclass scatter in a reproducing kernel Hilbert space. KDIsomap is used to perform nonlinear dimensionality reduction on the extracted local binary patterns (LBP) facial features, and produce low-dimensional discrimimant embedded data representations with striking performance improvement on facial expression recognition tasks. The nearest neighbor classifier with the Euclidean metric is used for facial expression classification. Facial expression recognition experiments are performed on two popular facial expression databases, i.e., the JAFFE database and the Cohn-Kanade database. Experimental results indicate that KDIsomap obtains the best accuracy of 81.59% on the JAFFE database, and 94.88% on the Cohn-Kanade database. KDIsomap outperforms the other used methods such as principal component analysis (PCA), linear discriminant analysis (LDA), kernel princip...Continue Reading

References

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Citations

Jun 28, 2012·Sensors·Shiqing ZhangBicheng Lei
Dec 10, 2013·Sensors·Muhammad Hameed SiddiqiPhan Tran Ho Truc
Nov 7, 2015·SpringerPlus·Pierluigi CarcagnìCosimo Distante
May 20, 2021·Computational Intelligence and Neuroscience·Yusra Khalid BhattiSergio A Velastin
Apr 18, 2018··R. MessoussiY. El Merabet

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Methods Mentioned

BETA
feature extraction
PCA

Software Mentioned

KIsomap Isomap
KDIsomap
KIsomap
KLDA
Isomap

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