%0 Journal Article
	%A Liew Yee Ping and  Pang Ying Han and  Lau Siong Hoe and  Ooi Shih Yin and  Housam Khalifa Bashier Babiker
	%D 2013
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 80, 2013
	%T Normalization Discriminant Independent Component Analysis
	%U https://publications.waset.org/pdf/16147
	%V 80
	%X 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.

	%P 1099 - 1103