@inproceedings{b96a8e1468b9443297c7180e5fa2b682,
title = "Discriminative common images for face recognition",
abstract = "Linear discrimination analysis (LDA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Basically, in LDA the image always needs to be transformed into ID vector, however recently twodimensional PCA (2DPCA) technique have been proposed. In 2DPCA, PCA technique is applied directly on the original images without transforming into ID vector. In this paper, we propose a new LDA-based method that applies the idea of two-dimensional PCA. In addition to that, our approach proposes an method called Discriminative Common Images based on a variation of Fisher's LDA for face recognition. Experiment results show our method achieves better performance in comparison with the other traditional LDA methods.",
keywords = "Discriminative Common Image, Face recognition, Fisherfaces, Linear discrimination analysis (LDA)",
author = "Nhat, \{Vo Dinh Minh\} and Sungyoung Lee",
note = "Copyright: Copyright 2008 Elsevier B.V., All rights reserved.; 15th International Conference on Artificial Neural Networks: Biological Inspirations, ICANN 2005 ; Conference date: 11-09-2005 Through 15-09-2005",
year = "2005",
doi = "10.1007/11550822\_88",
language = "English",
isbn = "3540287523",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "563--568",
booktitle = "Artificial Neural Networks",
address = "Germany",
}