A lyapunov theory-based neural network approach for face recognition

Li Minn Ang, King Hann Lim, Kah Phooi Seng, Siew Wen Chin

Research output: Chapter in Book or Report/Conference proceedingChapterpeer-review

4 Citations (Scopus)


This chapter presents a new face recognition system comprising of feature extraction and the Lyapunov theory-based neural network. It first gives the definition of face recognition which can be broadly divided into (i) feature-based approaches, and (ii) holistic approaches. A general review of both approaches will be given in the chapter. Face features extraction techniques including Principal Component Analysis (PCA) and Fisher's Linear Discriminant (FLD) are discussed. Multilayered neural network (MLNN) and Radial Basis Function neural network (RBF NN) will be reviewed. Two Lyapunov theory-based neural classifiers: (i) Lyapunov theory-based RBF NN, and (ii) Lyapunov theory-based MLNN classifiers are designed based on the Lyapunov stability theory. The design details will be discussed in the chapter. Experiments are performed on two benchmark databases, ORL and Yale. Comparisons with some of the existing conventional techniques are given. Simulation results have shown good performance for face recognition using the Lyapunov theory-based neural network systems.

Original languageEnglish
Title of host publicationIntelligent Systems for Automated Learning and Adaptation
Subtitle of host publicationEmerging Trends and Applications
PublisherIGI Global
Number of pages26
ISBN (Print)9781605667980
Publication statusPublished - 2009
Externally publishedYes

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