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A New Two-Directional Two-Dimensional Feature Extraction Based on Manifold Learning | IEEE Conference Publication | IEEE Xplore

A New Two-Directional Two-Dimensional Feature Extraction Based on Manifold Learning


Abstract:

A new feature extraction method based on manifold learning is proposed for face recognition in the paper; its criterion function is characterized by maximizing the differ...Show More

Abstract:

A new feature extraction method based on manifold learning is proposed for face recognition in the paper; its criterion function is characterized by maximizing the difference between the nonlocal scatter and the local scatter. The novel method is called two-directional two-dimensional marginal discriminant projection ((2D)2MDP), which simultaneously works image matrix in the row direction and in the column direction for feature extraction. The experimental results on ORL face databases indicate that the proposed method has higher recognition rate and more stable.
Date of Conference: 07-08 November 2009
Date Added to IEEE Xplore: 12 January 2010
ISBN Information:
Conference Location: Shanghai, China

I. INTRODUCTION

Feature extraction is the key to face recognition. The aim of feature extraction is to reduce the dimensionality of face image so that the extracted features are as representative as possible. When dealing with images, we should firstly transform the image matrixes into image vectors. Then based on these vectors, the between-class scatter matrix, within-class scatter matrix and total-class scatter matrix are calculated and the optimal projection axes are obtained. However, face image recognition is of high dimension and small sample size problem. The traditional FDA encounters two aspects of difficulties. First, the traditional algorithm cannot be used directly when the within-class scatter matrix is singular. Second, the high dimensional image vectors lead to computational difficulty. To overcome the drawback, Yang et al. [1] proposed a straightforward image projection technique, named image principal component analysis (i.e. 2DPCA); the method is to exploit image matrices to directly construct between class scatter matrix and within class scatter matrix.

References

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