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Face recognition using an enhanced independent component analysis approach.
- Source :
-
IEEE transactions on neural networks [IEEE Trans Neural Netw] 2007 Mar; Vol. 18 (2), pp. 530-41. - Publication Year :
- 2007
-
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.
- Subjects :
- Computer Simulation
Discriminant Analysis
Humans
Linear Models
Principal Component Analysis
Reproducibility of Results
Sensitivity and Specificity
Algorithms
Artificial Intelligence
Biometry methods
Face anatomy & histology
Image Enhancement methods
Image Interpretation, Computer-Assisted methods
Pattern Recognition, Automated methods
Subjects
Details
- Language :
- English
- ISSN :
- 1045-9227
- Volume :
- 18
- Issue :
- 2
- Database :
- MEDLINE
- Journal :
- IEEE transactions on neural networks
- Publication Type :
- Academic Journal
- Accession number :
- 17385637
- Full Text :
- https://doi.org/10.1109/TNN.2006.885436