@article{(Open Science Index):https://publications.waset.org/pdf/16147, title = {Normalization Discriminant Independent Component Analysis}, author = {Liew Yee Ping and Pang Ying Han and Lau Siong Hoe and Ooi Shih Yin and Housam Khalifa Bashier Babiker}, country = {}, institution = {}, 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. }, journal = {International Journal of Computer and Information Engineering}, volume = {7}, number = {8}, year = {2013}, pages = {1099 - 1103}, ee = {https://publications.waset.org/pdf/16147}, url = {https://publications.waset.org/vol/80}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 80, 2013}, }