Dostroajan: Facial Recognition Based System Input Control Agent

Trials were made to access the system with a total of 2800 photographs and as a result of the trials, success was achieved with a ratio of 76.31% in the worst angle and light and a ratio of 99.15% in the best angle and light.

📌 3 Key Takeaways

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Objective & Hypothesis:Speed, time and safety are of great importance in many operations conducted today.

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Key Findings & Data:There are standards such as ISO 27001, ITIL (Information Technologies Infrastructure Library), COBIT (Control Objectives for Information and Related Technology), which are globally recognized not only regarding access to information and the use of information but also information retention.

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Conclusion & Impact:Trials were made to access the system with a total of 2800 photographs and as a result of the trials, success was achieved with a ratio of 76.31% in the worst angle and light and a ratio of 99.15% in the best angle and light.

🔬Design:Research Article

📍Scope:Comprehensive Sample

⚙️Variables:Bilgi Güvenliği · Derin Öğrenme · Görüntü Tanıma

Abstract

Speed, time and safety are of great importance in many operations conducted today. There are standards such as ISO 27001, ITIL (Information Technologies Infrastructure Library), COBIT (Control Objectives for Information and Related Technology), which are globally recognized not only regarding access to information and the use of information but also information retention. Governmental institutions and many large companies use fingerprint, card reading, iris recognition and facial recognition systems in entrances and exits, regarding the protection of information.The facial recognition system application developed within the scope of this study performs the facial recognition by using Convolutional Neural Networks (CNN), which is one of the deep learning algorithms and restricts the use of your personal computer by people you do not know. In addition to this restriction, it takes a photo of the person who wants to use your personal computer and sends this photo to the mobile phone of the owner of the computer, who was previously defined in the system and informs him/her.Regarding the testing of the face recognition system application FEI (Faculdade de Engenharia Industrial- Faculty of Industrial Engineering) facial database was used. In this facial database, there are 14 different poses of 200 people (one is neutral, one is smiling, one is not smiling, and the others are at different angles). Trials were made to access the system with a total of 2800 photographs and as a result of the trials, success was achieved with a ratio of 76.31% in the worst angle and light and a ratio of 99.15% in the best angle and light.

Özet

Günümüzde yapılan birçok işlemde hız, zaman ve güvenlik büyük önem taşımaktadır. Bilgiye erişimin ve bilginin kullanımının yanı sıra bilginin saklanması noktasında küresel çapta kabul görmüş ISO 27001, ITIL (Information Technologies Infrastructure Library – Bilgi Teknolojisi Altyapı Kütüphanesi), COBIT (Control Objectives for Information and Related Technology - Bilgi ve İlgili Teknoloji İçin Kontrol Hedefleri) gibi standartlar vardır. Devlet kurumları ve birçok büyük şirket bilginin korunması hususunda giriş-çıkışlarda ve bu kurumların sistem odalarına erişimde parmak izi, kart okutma, iris tanıma ve yüz tanıma sistemleri kullanmaktadır.Bu çalışma kapsamında geliştirilen yüz tanıma sistemi uygulaması derin öğrenme algoritmalarından biri olan Evrişimsel Sinir Ağlarını (Convolutional Neural Networks - CNN) kullanarak, yüz tanıma işlemini gerçekleştirip, istenmeyen kişilerin kişisel bilgisayarı kullanmasını kısıtlamaktadır. Bu kısıtlamaya ek olarak kişisel bilgisayarları kullanmak isteyen kişinin fotoğrafını çekerek bu fotoğrafı sistemde daha önce tanımlanmış olan bilgisayar sahibinin cep telefonuna mesaj olarak gönderip bilgilendirme yapmaktadır.Yüz tanıma sistemi uygulamasının testi için FEI (Faculdade de Engenharia Industrial - Endüstri Mühendisliği Fakültesi) yüz veritabanı kullanılmıştır. Bu yüz veri tabanında 200 kişinin (biri nötr, biri gülümseyen, biri gülümsemeyen ve diğerleri de farklı açılarda olan) 14 farklı pozu bulunmaktadır. Toplamda 2800 fotoğraf ile sisteme erişim için denemeler yapıldı ve denemeler sonucunda en kötü açı ve ışık değerinde %76,31 ve en iyi açı ve ışık değerinde de %99,15 başarı sağlanmıştır.

Keywords

Bilgi Güvenliği Derin Öğrenme Görüntü Tanıma Güvenlik Ajan Information Security Deep Learning Image Recognition Security Agent

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Cite This Article

AYATA, F., ÇAVUŞ, H., İNAN, M., SEYYARER, E., BİÇEK, E., KINA, E. (2020). Dostroajan: Facial Recognition Based System Input Control Agent. AJIT-e: Academic Journal of Information Technology, Cilt: 11 Sayı: 40, 82–96. https://ajindex.com/articles/898991

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