This paper presents a kernel weighted scatter difference discriminant analysis (KWSDA) method for face recognition. This non-linear dimensionality reduction algorithm has several interesting characteristics. First, using a new optimization criterion it avoids small sample size problem intuitively. Second, by incorporating a weighting function into discriminant criterion, it overcomes overemphasis on well-separated classes and hence can work under more realistic situations. Lastly, applying kernel theory, it handles nonlinearity efficiently. Experiments on the ORL face database show that the proposed method is effective and feasible. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Chougdali, K., Jedra, M., & Zahid, N. (2008). Kernel weighted scatter-difference-based discriminant analysis for face recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5112 LNCS, pp. 977–983). https://doi.org/10.1007/978-3-540-69812-8_97
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