Abstract
As dimensionality reduction is an important problem in pattern recognition, it is necessary to reduce the dimensionality of the feature space for efficient face recognition. In this paper, we suggest the fusion of Discrete Wavelet Transform(DWT) and Direct Linear Discriminant Analysis (DLDA) for the efficient dimension reduction. The Support Vector Machines (SVM) and nearest mean classifier (NM) approaches are applied to compare the similarity between the similar and different face data. In the experiments, we show that the proposed method is an efficient way of representing face patterns as well as reducing dimension of multidimensional feature.
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© 2004 Springer-Verlag Berlin Heidelberg
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Son, B., Yoon, S., Lee, Y. (2004). Fusion of Dimension Reduction Methods and Application to Face Recognition. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_48
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DOI: https://doi.org/10.1007/978-3-540-30499-9_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23931-4
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