Enhancing Fingerprint Localization Accuracy With Inverse Weight-Normalized Context Similarity Coefficient-Based Fingerprint Similarity Metric

Abstract

Distance-based metrics are the most common fingerprint similarity metrics used in fingerprint database clustering and localization processes in a fingerprint-based localization system. In this paper, however, a less common but promising pattern-based fingerprint similarity metric is proposed as an alternative to the distance-base metric. The proposed fingerprint similarity metric is based on an inverse weight (IW) normalization of the context similarity coefficient (CSC)-based similarity metric measure. The clustering and localization performance of the fingerprint-based localization system with the proposed IW-CSC-based fingerprint similarity metric is determined and compared to the square Euclidean, Manhattan, and cosine distance-based metrics. The k-means algorithm with a k-means++ cluster initialization process is considered for fingerprint database clustering, while the k-nearest neighbor (k-NN) algorithm is considered for localization. Based on the four fingerprint databases considered, the proposed IW-CSC-based metric has the slowest localization time with moderate clustering performance. However, it has the best localization performance, which is at least 52% higher than the localization performances of the three distance-base metrics considered. The proposed IW-CSC-based metric is recommended as an alternative to the distancebase metric only when improved localization performance is the primary objective of the fingerprint-based localization system. It is also recommended for use in small to medium-sized fingerprint databases for clustering and localization.