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Research Letters in Signal Processing
Volume 2008 (2008), Article ID 486247, 5 pages
Research Letter

Extraction and Recognition of Nonlinear Interval-Type Features Using Symbolic KDA Algorithm with Application to Face Recognition

1Department of Studies in Computer Science, Gulbarga University, Gulbarga, 585-106 Karnataka, India
2Department of Studies in Computer Science, Kuvempu University, Shankaraghatta, 577-451 Karnataka, India

Received 21 July 2007; Accepted 12 February 2008

Academic Editor: Paul Cristea

Copyright © 2008 P. S. Hiremath and C. J. Prabhakar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We present symbolic kernel discriminant analysis (symbolic KDA) for face recognition in the framework of symbolic data analysis. Classical KDA extracts features, which are single-valued in nature to represent face images. These single-valued variables may not be able to capture variation of each feature in all the images of same subject; this leads to loss of information. The symbolic KDA algorithm extracts most discriminating nonlinear interval-type features which optimally discriminate among the classes represented in the training set. The proposed method has been successfully tested for face recognition using two databases, ORL database and Yale face database. The effectiveness of the proposed method is shown in terms of comparative performance against popular face recognition methods such as kernel Eigenface method and kernel Fisherface method. Experimental results show that symbolic KDA yields improved recognition rate.