Facial expression recognition using local binary patterns and discriminant kernel locally linear embedding

Xiaoming Zhao,Shiqing Zhang

doi:10.1186/1687-6180-2012-20

Xiaoming Zhao, Shiqing Zhang

Open Access

https://doi.org/10.1186/1687-6180-2012-20

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Abstract

Given the nonlinear manifold structure of facial images, a new kernel-based supervised manifold learning algorithm based on locally linear embedding (LLE), called discriminant kernel locally linear embedding (DKLLE), is proposed for facial expression recognition. The proposed DKLLE aims to nonlinearly extract the discriminant information by maximizing the interclass scatter while minimizing the intraclass scatter in a reproducing kernel Hilbert space. DKLLE is compared with LLE, supervised locally linear embedding (SLLE), principal component analysis (PCA), linear discriminant analysis (LDA), kernel principal component analysis (KPCA), and kernel linear discriminant analysis (KLDA). Experimental results on two benchmarking facial expression databases, i.e., the JAFFE database and the Cohn-Kanade database, demonstrate the effectiveness and promising performance of DKLLE.

Highlights

Affective computing, which is currently an active research area, aims at building the machines that recognize, express, model, communicate and respond to a user’s emotion information [1]
To tackle the above-mentioned problems of supervised locally linear embedding (SLLE), in this article a new kernel-based supervised manifold learning algorithm based on LLE, called discriminant kernel locally linear embedding (DKLLE), is proposed and applied for facial expression recognition
The performance of DKLLE is compared with LLE, SLLE, PCA, LDA, kernel principal component analysis (KPCA) [31], and kernel linear discriminant analysis (KLDA) [32]

Summary

Introduction

Affective computing, which is currently an active research area, aims at building the machines that recognize, express, model, communicate and respond to a user’s emotion information [1]. To tackle the above-mentioned problems of SLLE, in this article a new kernel-based supervised manifold learning algorithm based on LLE, called discriminant kernel locally linear embedding (DKLLE), is proposed and applied for facial expression recognition. DKLLE makes about 9% improvement over LLE and about 6% improvement over SLLE This demonstrates that DKLLE is able to extract the most discriminative low-dimensional embedded data representations for facial expression recognition. In [12], after extracting the most discriminative LBP (called boosted-LBP) features, they used SVM and separately obtained 7-class facial expression recognition accuracy of 79.8, 79.8, and 81.0% with linear, polynomial, and radial basis function (RBF) kernels.

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