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In this paper we present an analysis of face recognition system with a combination of Neural

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In this paper we present an analysis of face recognition system with a combination of Neural networks withSub-space method of feature extraction. Here we are considering both single layer such as Generalized Regression neural network (GRNN) and Multi layer such as Learning Vector quantization (LVQ). The analysis of these neural networks are done between feature vectors with respect to the recognition performance of subspace methods, namely Principal Component Analysis (PCA) and Fisher Linear Discriminant Analysis (FLDA).Subspace is a multiplex embedded in a higher dimensional vector space and extracting important features from the damn dimensionality. The experiments were performed using standard ORL, Yale and FERET database. From the …show more content…

In section IV, Experimental results are discussed and analysis is briefed. Finally, Conclusions are drawn.

II. PROPOSED METHOD
In this section an overview of different subspaces methods such as PCA and FLDA are describes in detail.

A. Principal Component Analysis

PCA is a classical feature extraction and data representation technique also known as Karhunen-Loeve Expansion [20, 21]. It is a linear method that projects the high-dimensional data onto a lower dimensional space. It seeks a weight projection that best represents the data, which is called principal components. Fig.1 Schematic illustration of PCA
Principal component analysis seeks a space of lower dimensionality, known as the principal subspace and denoted by the magenta line, such that the orthogonal projection of the data points (red dots) onto this subspace maximizes the variance of the projected points (green dots). An alternative definition of PCA is based on minimizing the sum-of-squares of the projection errors, indicated by the blue lines as described in figure 1.
PCA is described as let a face image be A(x, y) be a two-dimensional N by Narray. The training set images are mapped onto a collection of vector points in this huge space, these vector points are represented as subspace. These vector points are the eigen vectors which is obtained from the covariance matrix which defines the subspace of face images.Let the

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