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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F24%3A50021503" target="_blank" >RIV/62690094:18450/24:50021503 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10538328" target="_blank" >https://ieeexplore.ieee.org/document/10538328</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2024.3405350" target="_blank" >10.1109/ACCESS.2024.3405350</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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 distance-base 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. Authors

Název v anglickém jazyce

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

Popis výsledku anglicky

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 distance-base 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. Authors

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

12

Číslo periodika v rámci svazku

June

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

73642-73651

Kód UT WoS článku

001237414400001

EID výsledku v databázi Scopus

2-s2.0-85194082838