Derin Sinir Ağlarını Kullanarak İnsan Yüzü Tanıma

Yıl 2022, Cilt: 4 Sayı: 2, 189 - 198, 26.10.2022

Kaplan Kaplan , Fatma Kuncan

https://doi.org/10.46387/bjesr.1139029

Öz

Son yıllarda birçok araştırmacı farklı uygulamalar için yapay zeka uygulamalarını içeren bilgisayar tabanlı sistemler kullanmaktadır. Kişi tanıma uygulaması da bu alanda yapılan çalışmalardandır. İlk zamanlarda güvenlik önlemleri için tasarlanan yüz ve nesne tanıma uygulamaları, son zamanlarda eğlence ve alışveriş sektörü alanlarında da kullanılmaktadır. Bu uygulamalar, çeşitli firmaların mobil uygulama geliştirmeleriyle daha da popülerlik kazanmaktadır. Yüz tanıma uygulamalarında, verilerin büyük ve karmaşık olması durumunda derin öğrenme yöntemleri tercih edilebilmektedir. Bu çalışmada da bir yüz tanıma uygulaması için 3 katmanlı bir Evrişimli Sinir Ağı (ESA) geliştirilmiştir. Geliştirilen model Libor Spacek's Facial Images Databases veri setine uygulanmıştır. Önerilen yöntemin veri seti üzerine uygulanması sonucunda %99.29 doğruluk oranı olduğu belirlenmiştir. Bu da uygulamanın gerçek bir tanıma sistemine uyarlanabileceği anlamına gelmektedir.

Anahtar Kelimeler

Derin öğrenme, Evrişimli Sinir Ağları, Yüz Tanıma, Faces96

Human Face Recognition Using Deep Neural Networks

Öz

In recent years, many researchers have been using computer-based systems containing artificial intelligence applications for different applications. Human recognition application is one of the studies carried out in this field. Face and object recognition applications, which were originally designed for security measures, are also used in the entertainment and shopping sectors recently. These applications are gaining even more popularity with the mobile application development of various companies. In face recognition applications, deep learning methods can be preferred if the data is large and complex. In this study, a 3-layer Convolutional Neural Network (CNN) has been developed for a face recognition application. The developed model was applied to the Libor Spacek's Facial Images Databases dataset. As a result of the application of the proposed method on the data set, it was determined that the accuracy rate was 99.29%. This means that the application can be adapted for real recognition systems.

Anahtar Kelimeler

Deep Learning, Convolutional Neural Networks, Face Recognition, Faces96

Kaynakça

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