Face recognition using an enhanced independent component analysis approach.

Full Abstract

This paper is concerned with an enhanced independent component analysis (ICA) and its application to face recognition. Typically, face representations obtained by ICA involve unsupervised learning and high-order statistics. In this paper, we develop an enhancement of the generic ICA by augmenting this method by the Fisher linear discriminant analysis (LDA); hence, its abbreviation, FICA. The FICA is systematically developed and presented along with its underlying architecture. A comparative analysis explores four distance metrics, as well as classification with support vector machines (SVMs). We demonstrate that the FICA approach leads to the formation of well-separated classes in low-dimension subspace and is endowed with a great deal of insensitivity to large variation in illumination and facial expression. The comprehensive experiments are completed for the facial-recognition technology (FERET) face database; a comparative analysis demonstrates that FICA comes with improved classification rates when compared with some other conventional approaches such as eigenface, fisherface, and the ICA itself.

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Author information

Author/s: Kwak, Keun-Chang (KC); Pedrycz, Witold (W);

Affiliation: Intelligent Robot Division, Electronics and Telecommunications Research Institute (ETRI), Daejeon 305-350, Korea. kwak(-atsign-)etri.re.kr

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council (IEEE Trans Neural Netw), published in United States. (Language: eng)

Reference: 2007-Mar; vol 18 (issue 2) : pp 530-41

Dates: Created 2007/03/27; Completed 2007/04/24;

PMID: 17385637, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms
Artificial Intelligence
Biometry - methods
Computer Simulation
Discriminant Analysis
Face - anatomy & histology
Humans

Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Linear Models
Pattern Recognition, Automated - methods
Principal Component Analysis
Reproducibility of Results
Sensitivity and Specificity

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