Czasopismo | PRZEGLĄD ELEKTROTECHNICZNY | Rocznik 2024 - zeszyt 9

Gabor, LBP, and BSIF features: Which is more appropriate for finger-knuckles-print recognition?

Funkcje Gabor, LBP i BSIF: Która z nich jest bardziej odpowiednia do rozpoznawania odcisków palców i kostek?)

10.15199/48.2024.09.10

nr katalogowy: 150349
10.15199/48.2024.09.10

Streszczenie

An accurate personal identification system helps control access to secure information and data. Biometric technology mainly focuses on the physiological or behavioural characteristics of the human body. This paper investigates the Finger Knuckle Print (FKP) biometric device based on the feature extraction technique. This FKP authentication method includes all the essential processes, such as preprocessing, feature extraction and classification. The features of the FKP application are investigated. Finally, this paper proposes the selection of the best feature extraction based on FKP recognition efficiency. The primary purpose of this paper is to use the Local Binary Patterns (LBP), Binarized Statistical Image Features (BSIF), and Gabor filters and define which helps to increase the False Acceptability Rate (FAR) and Genuine Acceptability Rate (GAR). This latest FKP selection shows better results as this concept shows promising results in recognizing a person's fingerknuckle print.

Abstract

Dokładny system identyfikacji osobistej pomaga kontrolować dostęp do bezpiecznych informacji i danych. Technologia biometryczna koncentruje się głównie na cechach fizjologicznych lub behawioralnych ludzkiego ciała. W artykule zbadano urządzenie biometryczne typu Finger Knuckle Print (FKP) oparte na technice ekstrakcji cech. Ta metoda uwierzytelniania FKP obejmuje wszystkie niezbędne procesy, takie jak przetwarzanie wstępne, ekstrakcja cech i klasyfikacja. Badane są funkcje aplikacji FKP. Na koniec w artykule zaproponowano wybór najlepszej ekstrakcji cech w oparciu o efektywność rozpoznawania FKP. Głównym celem tego artykułu jest wykorzystanie lokalnych wzorców binarnych (LBP), binarnych cech obrazu statystycznego (BSIF) i filtrów Gabora oraz zdefiniowanie, które pomagają zwiększyć współczynnik fałszywej akceptowalności (FAR) i współczynnik prawdziwej akceptowalności (GAR). Najnowsza selekcja FKP zapewnia lepsze wyniki, ponieważ koncepcja ta zapewnia obiecujące wyniki w rozpoznawaniu odcisków palców danej osoby.

Słowa kluczowe

Keywords

Bibliografia

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