Fusion of Dimension Reduction Methods and Application to Face Recognition
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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- 2.Jollife, I.T.: Principal Component Analysis. Springer, New York (1986)Google Scholar
- 3.Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification and Scene Analysis. Wiley Interscience, Hoboken (2000)Google Scholar
- 7.Tefas, A., Kotropoulos, C., Pitas, I.: Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(7) (2001)Google Scholar
- 8.Laboratories of Intelligent Systems, Institute of Information Science. The IIS Face Database, http://smart.iis.sinica.edu.tw/index.html