Abstract
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.
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Chougdali, K., Jedra, M., Zahid, N. (2008). Kernel Weighted Scatter-Difference-Based Discriminant Analysis for Face Recognition. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2008. Lecture Notes in Computer Science, vol 5112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69812-8_97
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DOI: https://doi.org/10.1007/978-3-540-69812-8_97
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69811-1
Online ISBN: 978-3-540-69812-8
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