Next Article in Journal
Automated Data Quality Assessment of Marine Sensors
Previous Article in Journal
Photoinduced Electron Transfer Based Ion Sensing within an Optical Fiber
Article Menu

Export Article

Open AccessArticle
Sensors 2011, 11(10), 9573-9588; doi:10.3390/s111009573

Facial Expression Recognition Based on Local Binary Patterns and Kernel Discriminant Isomap

1
Department of Computer Science, Taizhou University, Taizhou 317000, China
2
School of Physics and Electronic Engineering, Taizhou University, Taizhou 318000, China
*
Author to whom correspondence should be addressed.
Received: 31 August 2011 / Revised: 27 September 2011 / Accepted: 9 October 2011 / Published: 11 October 2011
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [587 KB, uploaded 21 June 2014]   |  

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 principal component analysis (KPCA), kernel linear discriminant analysis (KLDA) as well as kernel isometric mapping (KIsomap). View Full-Text
Keywords: kernel; isometric mapping; dimensionality reduction; local binary patterns; facial expression recognition kernel; isometric mapping; dimensionality reduction; local binary patterns; facial expression recognition
Figures

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Zhao, X.; Zhang, S. Facial Expression Recognition Based on Local Binary Patterns and Kernel Discriminant Isomap. Sensors 2011, 11, 9573-9588.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top