In this article, a face recognition system using the Principal Component Analysis (PCA) algorithm was implemented. The algorithm is based on an eigenfaces approach which represents a PCA method in which a small set of significant features are used to describe the variation between face images. Experimental results for different numbers of eigenfaces are shown to verify the viability of the proposed method

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In image processing edges are observed as singularities. Calculating gradient is regular procedure for locating edges within the image. However, there are edges with different order of that given by gradient. Hӧlder exponent gives mathematical background for different types of edges considering differentiation. In this paper we analyze some well known capacities, needed in pointwise Hӧlder exponent calculation, and introduce a new capacity measure based on local image statistics.

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