Euclidean Distance Versus Manhattan Distance for New Representative SFA Skin Samples for Human Skin Segmentation

Abstract

The human color skin image database called SFA, specifically designed to assist research in the area of face recognition, constitutes a very important means particularly for the challenging task of skin detection. It has showed high performances comparing to other existing databases. SFA database provides multiple skin and non-skin samples, which in various combinations with each other allow creating new samples that could be useful and more effective. This particular aspect will be investigated, in the present paper, by creating four new representative skin samples according to the four rules of minimum, maximum, mean and median. The obtained samples will be exploited for the purpose of skin segmentation on the basis of the well-known Euclidean and Manhattan distance metrics. Thereafter, performances of the new representative skin samples versus performances of those skin samples, originally provided by SFA, will be illustrated. Simulation results in both SFA and UTD (University of Texas at Dallas) color face databases indicate that detection rates higher than 92% can be achieved with either measure.

Group of All Taxicab Isometries: A Combinatorial Approach

“…Theorem 2. Suppose φ ∈ Fix O (2). If φ leaves all vertices of S r fixed, then each point on the x-axis and y-axis is a fixed-point of φ.…”

“…Theorem 3. Let φ be any isometry in Fix O (2). If all vertices of S r are fixed-points of φ, then every point on S r is a fixed-points of φ.…”

“…Corollary 1. Let φ be any isometry in Fix O (2). If all vertices of S r are fixed-points of φ, for some r > 0, then φ is an identity.…”

“…Minkowski distance is a large family of mathematical distance functions used to measure the distance between two points in a multi-dimensional space. The Minkoswski distances are keys for many applications in many areas, such as image processing [1][2][3], biometric, fingerprint, and face recognition [4][5][6][7], data mining [8,9], tomography [10][11][12][13], urban planning [14], and network analysis [15].…”

Comparative Analysis of Euclidean, Manhattan, Canberra, and Squared Chord Methods in Face Recognition

“…At this stage, the classification is carried out using the distance function by measuring the similarity or difference between one pattern and another [19,20]. The pattern used in this study was obtained from histogram data.…”