A framework for radial data comparison and its application to fingerprint analysis

Abstract

This work tackles the comparison of radial data, and proposes comparison measures that are further applied to fingerprint analysis. First, we study the similarity of scalar and non-scalar radial data, elaborated on previous works in fuzzy set theory. This study leads to the concepts of Restricted Radial Equivalence Function and Radial Similarity Measure, which model the perceived similarity between scalar and vectorial pieces of radial data, respectively. Second, the utility of these functions is tested in the context of fingerprint analysis, and more specifically, in the singular point detection. With this aim, a novel template-based singular point detection method is proposed, which takes advantage of these functions. Finally, their suitability is tested in different fingerprint databases. Different similarity measures are considered to show the flexibility offered by these measures and the behavior of the new method is compared with well-known singular point detection methods.

From Restricted Equivalence Functions on $L^{n}$ to Similarity Measures Between Fuzzy Multisets

“…Since its introduction, the concept of REF has been further adapted to compare non-scalar data. Relevant examples are the Interval-valued REFs (IV-REFs), designed to compare interval-valued membership degrees [11], or the Radial REFs (RREFs), tailored to scalar data in radial setups [12]. A critical need in the adaptation of REFs to scenarios other than its original one relate to the modelling of the monotonicity in data, which is critically used in the axiomatic definition of REFs.…”

A BP Artificial Neural Network Model for Earthquake Magnitude Prediction in Himalayas, India

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