Title: Face Recognition Using Linear Discriminant Analysis (LDA) of Principal Component Analysis (PCA)
Abstract: In this paper, a face recognition is presented based on PCA (principal Component Analysis) and LDA (Linear Discriminant Analysis). The recognition method involves two steps: first using PCA to find an orthonormal basis for the subspace containing all the face features from the training face database and second using LDA to obtain the best linear classifier of the face database. Most of the traditional LDA-based methods suffer from the disadvantage that their classification performance is not directly related to the classification accuracy ability of the obtained feature representation. Moreover, their classification accuracy is affected by the small sample size (SSS) problem which is often encountered in the face recognition tasks. For this, the proposed LDA of PCA algorithm within the paper is by introducing the weighting functions into LDA. The main idea of the paper is to demonstrate the recognition performance of proposed algorithm using the Intelligent Control Laboratory (lCONL) database. A comparison of recognition performance between the original LDA of PCA to the LDA of PCA with weighting function is carried out.
Publication Year: 2007
Publication Date: 2007-09-01
Language: en
Type: article
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Cited By Count: 8
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