Discrete Dynamics in Nature and Society
Volume 2009 (2009), Article ID 916382, 8 pages
Research Article

Locally Linear Discriminate Embedding for Face Recognition

Faculty of Information Science and Technology, Multimedia University, 75450 Melaka, Malaysia

Received 21 January 2009; Accepted 12 October 2009

Academic Editor: B. Sagar

Copyright © 2009 Eimad E. Abusham and E. K. Wong. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A novel method based on the local nonlinear mapping is presented in this research. The method is called Locally Linear Discriminate Embedding (LLDE). LLDE preserves a local linear structure of a high-dimensional space and obtains a compact data representation as accurately as possible in embedding space (low dimensional) before recognition. For computational simplicity and fast processing, Radial Basis Function (RBF) classifier is integrated with the LLDE. RBF classifier is carried out onto low-dimensional embedding with reference to the variance of the data. To validate the proposed method, CMU-PIE database has been used and experiments conducted in this research revealed the efficiency of the proposed methods in face recognition, as compared to the linear and non-linear approaches.