Classification of Ear Images According to Person, Age, and Gender with The Local Ternary Pattern

Year 2022, Volume: 27 Issue: 3, 1003 - 1022, 31.12.2022

Emrah Aydemir , Asaad Qais Shalal Abo Soot

https://doi.org/10.17482/uumfd.1056921

Abstract

The need to verify the identity of individuals is increasing day by day. Traditionally, passports, identity cards, keys are used in authentication systems. With such systems, passwords can also be used to increase security. Unfortunately, the disadvantages of such security systems include the loss, copying, and theft of the item used as an identity. Passwords can be forgotten. Such situations can endanger the person or put him in a difficult situation. Such shortcomings of traditional person recognition techniques cause major problems for everyone. Such situations push researchers to seek a solid, reliable and perfect personal description. This search pushes researchers to biometric systems. In this study, 2000 data, which are right and left ear images of 100 people, were collected. The attributes of these collected files were extracted with the Local Triple Pattern. For each image file, 1x512 vectors were produced. These processes were performed for all files and images were classified for person, age and gender with many different classification algorithms. While 90.2% accuracy rate was obtained for person recognition, 99.8% success was achieved for gender. Finally, the classification success rate was 86.1% for age.

Keywords

Ear image, local ternary pattern, classification

YEREL ÜÇLÜ DESEN İLE KULAK GÖRÜNTÜLERİNİN KİŞİ, YAŞ VE CİNSİYETE GÖRE SINIFLANDIRILMASI

Abstract

Bireylerin kimliğini doğrulamaya yönelik ihtiyaç her geçen gün artmaktadır. Geleneksel olarak kimlik doğrulama sistemlerinde pasaportlar, kimlik kartları, anahtarlar kullanılır. Bu tür sistemler ile birlikte güvenliği arttırmak için şifreler de kullanılabilir. Maalesef bu tür güvenlik sistemlerinin dezavantajları arasında kimlik olarak kullanılan eşyanın kaybolması, kopyalanması, çalınması söz konusu olabilir. Şifrelerin ise unutulması ortaya çıkabilir. Bu tür durumlar kişiyi tehlikeye atabilir veya zor bir duruma sokabilir. Geleneksel kişi tanıma tekniklerinin bu tür eksiklikleri, herkes için büyük sorunlara neden olur. Bu tür durumlar ise araştırmacıları sağlam, güvenilir ve kusursuz bir kişisel tanımlama arayışına itmektedir. Bu arayış ise araştırmacıları biyometri sistemlerine itmektedir. Buradaki çalışma da 100 kişiye ait sağ ve sol kulak görüntüleri olan 2000 veri toplanmıştır. Toplanan bu dosyaların Yerel Üçlü Desen ile öznitelikleri çıkarılmıştır. Her bir görüntü dosyası için 1x512 boyutlarında vektör üretilmiştir. Tüm dosyalar için bu işlemler yapılmış ve birçok farklı sınıflandırma algoritmaları ile görüntüler kişi, yaş ve cinsiyet için sınıflandırılmıştır. Kişi tanıma için % 90,2 oranında doğruluk oranı elde edilirken, cinsiyet için % 99,8 oranında başarı elde edilmiştir. Son olarak yaş için ise % 86,1 oranında sınıflandırma başarısına ulaşılmıştır.

Keywords

kulak görüntüsü, yerel üçlü desen, sınıflandırma

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