Citation

Liew, Yee Ping and Pang, Ying Han and Lau, Siong Hoe and Ooi, Shih Yin and Bashier, Housam Khalifa (2013) Normalization discriminant independent component analysis. International Journal of Computer, Information Science and Engineering, 7 (8). pp. 143-147. ISSN 2010-376X

Text Normalization Discriminant Independent Component.pdf Restricted to Repository staff only Download (193kB)

Abstract

In face recognition, feature extraction techniques attempts to search for appropriate representation of the data. However, when the feature dimension is larger than the samples size, it brings performance degradation. Hence, we propose a method called Normalization Discriminant Independent Component Analysis (NDICA). The input data will be regularized to obtain the most reliable features from the data and processed using Independent Component Analysis (ICA). The proposed method is evaluated on three face databases, Olivetti Research Ltd (ORL), Face Recognition Technology (FERET) and Face Recognition Grand Challenge (FRGC). NDICA showed it effectiveness compared with other unsupervised and supervised techniques.

Item Type:	Article
Uncontrolled Keywords:	Face recognition, small sample size, regularization, independent component analysis
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Mar 2014 09:15
Last Modified:	08 Dec 2022 05:00
URII:	http://shdl.mmu.edu.my/id/eprint/5352

Downloads

Edit (login required)