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Linear Discriminant Analysis (LDA) is one of the principal techniques used in face recognition systems. The Linear Discriminant Analysis (LDA) is well-known scheme for feature extraction and dimension reduction. It provides improved performance over the standard Principal Component Analysis (PCA) method of face recognition by introducing the concept of classes and distance between classes. This paper provides an overview of PCA, the various variants of LDA and their basic drawbacks. The proposed method includes a development over classical LDA (i.e. LDA using wavelets transform approach) that enhances performance such as accuracy and time complexity. Experiments on ORL face database clearly demonstrate this and the graphical comparison of the algorithms clearly showcases the improved recognition rate in case of the proposed algorithm.
Date of Conference: 10-12 Jan. 2012