IMAGE FUSION AND DEEP LEARNING BASED EAR RECOGNITION USING THERMAL AND VISIBLE IMAGES

Mücahit Cihan; Murat Ceylan

doi:10.17780/ksujes.1345020

TR EN

TERMAL VE GÖRÜNÜR GÖRÜNTÜLER KULLANILARAK GÖRÜNTÜ BİRLEŞTİRME VE DERİN ÖĞRENME TABANLI KULAK TANIMA

Öz

Görüntüleme ve derin öğrenme alanındaki gelişmeler, kulağın yapısı benzersiz tanımlama özellikleri sunduğundan kulak biyometrisine olan ilgiyi artırmıştır. Termal ve görünür kulak görüntüleri bu özelliklerin farklı yönlerini yakalar. Termal görüntüler ışıktan bağımsızdır ve görünür görüntüler doku ayrıntılarını yakalamada mükemmeldir. Bu görüntülerin birleştirilmesi daha zengin özelliklere sahip kompozit görüntüler oluşturur. Bu çalışma, otomatik kulak tanımayı geliştirmek amacıyla farklı aydınlatma koşulları altında elde edilen termal ve görünür kulak görüntülerinin birleştirilmesini incelemektedir. Görüntü birleştirme işlemi üç farklı çok çözünürlüklü analiz yöntemini içermektedir: ayrık dalgacık dönüşümü, ridgelet dönüşümü ve curvelet dönüşümü. Ardından, kulak tanıma için özel olarak tasarlanmış derin öğrenme modeli kullanılmıştır. Bu çalışmanın sonuçları, karmaşık değerli curvelet dönüşümü ve termal görüntülerin kullanılmasının, diğer tüm yöntemleri geride bırakarak %96.82 gibi etkileyici bir tanıma oranı elde ettiğini ortaya koymaktadır. Buna karşılık, görünür görüntüler özellikle düşük ışık koşullarında %75.00 ile en düşük tanıma oranını sergilemiştir. Sonuç olarak, birden fazla veri kaynağının birleştirilmesi kulak tanıma etkinliğini önemli ölçüde artırmaktadır ve önerilen model, sınırlı sayıda birleştirilmiş kulak görüntüsüyle çalışırken bile tutarlı bir şekilde dikkate değer tanıma oranlarına ulaşmaktadır.

Anahtar Kelimeler

IMAGE FUSION AND DEEP LEARNING BASED EAR RECOGNITION USING THERMAL AND VISIBLE IMAGES

Abstract

Advances in imaging and deep learning have fueled interest in ear biometrics, as the structure of the ear offers unique identification features. Thermal and visible ear images capture different aspects of these features. Thermal images are light-independent, and visible images excel at capturing texture details. Combining these images creates more feature-rich composite images. This study examines the fusion of thermal and visible ear images taken under varying lighting conditions to enhance automatic ear recognition. The image fusion process involved three distinct multiresolution analysis methods: discrete wavelet transform, ridgelet transform, and curvelet transform. Subsequently, a specially designed deep learning model was used for ear recognition. The results of this study reveal that employing the complex-valued curvelet transform and thermal images achieved an impressive recognition rate of 96.82%, surpassing all other methods. Conversely, visible images exhibited the lowest recognition rate of 75.00%, especially in low-light conditions. In conclusion, the fusion of multiple data sources significantly enhances ear recognition effectiveness, and the proposed model consistently achieves remarkable recognition rates even when working with a limited number of fused ear images.

Keywords

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Görüntü İşleme , Örüntü Tanıma , Derin Öğrenme

Bölüm

Araştırma Makalesi

Yazarlar

Mücahit Cihan ^*
0000-0002-1426-319X
Türkiye

Murat Ceylan
0000-0001-6503-9668
Türkiye

Yayımlanma Tarihi

3 Aralık 2023

Gönderilme Tarihi

17 Ağustos 2023

Kabul Tarihi

21 Eylül 2023

Yayımlandığı Sayı

Yıl 2023 Cilt: 26 Sayı: 4

DOI

https://doi.org/10.17780/ksujes.1345020

IZ

https://izlik.org/JA45JP32RP

Kaynak Göster

RIS / Bibtex

APA

Cihan, M., & Ceylan, M. (2023). IMAGE FUSION AND DEEP LEARNING BASED EAR RECOGNITION USING THERMAL AND VISIBLE IMAGES. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 26(4), 997-1009. https://doi.org/10.17780/ksujes.1345020