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2015•BENTHAM SCIENCE PUBLISHERS eBooksRequires access

Minkowski Distances for Face Recognition

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Abstract

Minkowski distances really deserve a whole chapter for theirselves. Depending on the value choice of parameter p, explained here below in the introduction, the concept of Minkowski distance is split up in different distance measures, which are typically known as taxicab (p=1), Euclidean (p=2), and Chebyshev distances (􀝌 = ∞). These measures have been widely employed in the 2D face recognition context, as the section dealing with performances outlines.

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What this paper is about

Minkowski distances really deserve a whole chapter for theirselves. Depending on the value choice of parameter p, explained here below in the introduction, the concept of Minkowski distance is split up in different distance measures, which are typically known as taxicab (p=1), Euclidean (p=2), and Chebyshev distances (􀝌 = ∞). These measures have been widely employed in the 2D face recognition context, as the section dealing with performances outlines.

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Available abstract

Minkowski distances really deserve a whole chapter for theirselves. Depending on the value choice of parameter p, explained here below in the introduction, the concept of Minkowski distance is split up in different distance measures, which are typically known as taxicab (p=1), Euclidean (p=2), and Chebyshev distances (􀝌 = ∞). These measures have been widely employed in the 2D face recognition context, as the section dealing with performances outlines.

Key concepts: Minkowski space, Minkowski distance, Face (sociological concept), Euclidean geometry, Context (archaeology), Mathematics, Euclidean distance, Section (typography)

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