Transform Based Feature Extraction and Dimensionality Reduction Techniques

Transform Based Feature Extraction and Dimensionality Reduction Techniques

Dattatray V. Jadhav (Vishwakarma Institute of Technology, India), V. Jadhav Dattatray (Vishwakarma Institute of Technology, India), Raghunath S. Holambe (SGGS Institute of Engineering and Technology, India) and S. Holambe Raghunath (SGGS Institute of Engineering and Technology, India)
Copyright: © 2011 |Pages: 17
DOI: 10.4018/978-1-61520-991-0.ch007
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Abstract

Various changes in illumination, expression, viewpoint, and plane rotation present challenges to face recognition. Low dimensional feature representation with enhanced discrimination power is of paramount importance to face recognition system. This chapter presents transform based techniques for extraction of efficient and effective features to solve some of the challenges in face recognition. The techniques are based on the combination of Radon transform, Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT). The property of Radon transform to enhance the low frequency components, which are useful for face recognition, has been exploited to derive the effective facial features. The comparative study of various transform based techniques under different conditions like varying illumination, changing facial expressions, and in-plane rotation is presented in this chapter. The experimental results using FERET, ORL, and Yale databases are also presented in the chapter.
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Feature selection for face representation is one of central issues to face recognition systems. Appearance based approaches, which generally operate directly on images or appearances of face objects, process the images as two-dimensional holistic patterns. Principle component analysis (PCA) and linear discriminant analysis (LDA) are widely used subspace analyses for data reduction and feature extraction in appearance-based approaches. Most of the appearance based feature extraction techniques can be classified into following types.

  • Algorithms based on principal component analysis (PCA)

  • Algorithms based on nonlinear PCA

  • Algorithms based on linear discriminant analysis

  • Algorithms based on nonlinear discriminant analysis

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