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Dostroajan: Yüz Tanıma Tabanlı Sistem Giriş Kontrol Ajanı

Yıl 2020, Cilt: 11 Sayı: 40, 82 - 96, 03.05.2020

Faruk Ayata Hayati Çavuş Mevlüt İnan Ebubekir Seyyarer Emre Biçek Erol Kına

https://doi.org/10.5824/ajite.2020.01.005.x

Öz

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.

Anahtar Kelimeler

Bilgi Güvenliği Derin Öğrenme Görüntü Tanıma Güvenlik Ajan

Kaynakça

  • Ahmed, E., Jones, M., & Marks, T. K. (2015). An Improved Deep Learning Architecture For Person Re-Identification. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3908-3916).
  • Catalina P., Useche M., Javier O. Pinzo Arenas and Robinson Jimeez Moreno. (2018). Face Recognition Access Control System using Convolutional Neural Networks. Research Journal of Applied Sciences, 13: 47-53.
  • Cengil, E., Çinar, A.,(2016).“A New Approach For Image Classification: Convolutional Neural Network”, European Journal of Technique (EJT), 6 (2), 96-103.
  • Chahar, H., & Nain, N. (2017, December). A Study on Deep Convolutional Neural Network Based Approaches for Person Re-identification. In International Conference on Pattern Recognition and Machine Intelligence (pp. 543-548). Springer, Cham.
  • Erdem, M. E., & Topal, C. (2018, May). Patch Warping Based Face Frontalization. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
  • Geng, M., Wang, Y., Xiang, T., Tian, Y. (2016). Deep Transfer Learning For Person Reidentification. arXiv preprint arXiv:1611.05244 .
  • Guo S., S. Chen and Y. Li. (2016). Face Recognition Based On Convolutional Neural Network And Support Vector Machine. IEEE International Conference on Information and Automation (ICIA), Ningbo, 2016, pp. 1787-1792. doi: 10.1109/ICInfA.2016.7832107.
  • İnik, Ö., Ülker, E., (2017). “Derin Öğrenme Ve Görüntü Analizinde Kullanılan Derin Öğrenme Modelleri”, Gaziosmanpaşa Bilimsel Araştırma Dergisi , 6 (3) , 85-104.
  • Kaplan, A. (2018). Gerçek ve Yarı Gerçek Zamanlı Yüz Tespit Etme/Face Detection On Real And Semi-Real Time. Fırat Üniversitesi Fen Bilimleri Enstitüsü, Yüksek Master Thesis. Elazığ.
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet Classification With Deep Convolutional Neural Networks. In Advances in neural information processing systems 25 (NIPS 2012) (pp. 1097-1105).
  • Li, W., Zhao, R., Xiao, T., Wang, X. (2014). Deepreid: deep filter pairing neural network for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 152–159). Liu, H., Feng, J., Qi, M., Jiang, J., Yan, S. (2016). End-to-end comparative attention networks for person re-identification, arXiv preprint arXiv:1606.04404.
  • Pala, T., Yücedağ, İ., Kahraman, H. T., Güvenç, U., & Sönmez, Y. (2018, September). Haar Wavelet Neural Network Model. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP) (pp. 1-8). IEEE.
  • Rashid, E. (2018). Raspberry Pi Ile Gerçek Zamanlı Yüz Tanıma Ve Kontrol Sistemi. Doctoral dissertation, Selçuk Üniversitesi Fen Bilimleri Enstitüsü. Konya.
  • Sharma, K. Shanmugasundaram and S. K. Ramasamy. (2016). FAREC — CNN based efficient face recognition technique using Dlib. International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), Ramanathapuram, 2016, pp. 192-195. doi: 10.1109/ICACCCT.2016.7831628.
  • Sharma, R., Ashwin, T. S., & Guddeti, R. M. R. (2019). A Novel Real-Time Face Detection System Using Modified Affine Transformation and Haar Cascades. In Recent Findings in Intelligent Computing Techniques (pp. 193-204). Springer, Singapore.
  • Su, C., Zhang, S., Xing, J., Gao, W., Tian, Q. (2016). Deep attributes driven multicamera Person re-identification. ECCV 2016. LNCS, vol. 9906, pp. 475–491. Springer, Cham Doi:10. 1007/978-3-319-46475-6 30.
  • Taşova, O., (2011). Yapay Sinir Ağları Ile Yüz Tanıma. Dokuz Eylül Üniversitesi Fen Bilimleri Enstitüsü Yüksek Lisans Tezi. İzmir.
  • Varior, R.R., Haloi, M., Wang, G. (2016). Gated Siamese convolutional neural network architecture for human re-identification. ECCV 2016. LNCS (vol. 9912, pp. 791–808). Springer, Cham Doi:10.1007/978-3-319-46484-8 48.
  • Vinay A. et al., (2017). G-CNN and F-CNN: Two CNN based architectures for face recognition. International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), Chirala (pp. 23-28). doi: 10.1109/ICBDACI.8070803
  • Wang, F., Zuo, W., Lin, L., Zhang, D., Zhang, L. (2016). Joint learning of single-image and cross-image representations for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1288–1296).
  • Wu, L., Shen, C., & van den Hengel, A. (2016). Convolutional LSTM networks for video-based person re-identification. arXiv preprint arXiv:1606.01609, 1(11).
  • Xiao, T., Li, H., Ouyang, W., Wang, X. (2016). Learning deep feature representations with domain guided dropout for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1249–1258).
  • Yaman, A. U., & Samet, R. D. (2018). Yüz tanıma sistemlerinin yanıltılmasına karşı bir yöntem: Yüz videolarında nabız tespiti ile canlılık doğrulaması, Ankara üniversitesi Fen Bilimleri Enstitüsü, Ankara.
  • Yang, M., David J. Kriegman, and Narendra Ahuja. (2002). “Detecting Faces in Images: A Survey”, IEEE Trans. Pattern Anal. Mach. Intell. 24, 1 (January 2002), 34–58. DOI:https://doi.org/10.1109/34.982883.
  • Yi, D., Lei, Z., Liao, S., Li, S.Z. (2014). Deep metric learning for person re-identification. In: Proceedings of International Conference on Pattern Recognition (pp. 2666–2672).
  • Zhang, Z., Si, T., & Liu, S. (2018). Integration convolutional neural network for person re-identification in camera networks. IEEE Access, 6, 36887-36896.

Dostroajan: Facial Recognition Based System Input Control Agent

Yıl 2020, Cilt: 11 Sayı: 40, 82 - 96, 03.05.2020

Faruk Ayata Hayati Çavuş Mevlüt İnan Ebubekir Seyyarer Emre Biçek Erol Kına

https://doi.org/10.5824/ajite.2020.01.005.x

Öz

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.

Anahtar Kelimeler

Information Security Deep Learning Image Recognition Security Agent

Kaynakça

  • Ahmed, E., Jones, M., & Marks, T. K. (2015). An Improved Deep Learning Architecture For Person Re-Identification. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3908-3916).
  • Catalina P., Useche M., Javier O. Pinzo Arenas and Robinson Jimeez Moreno. (2018). Face Recognition Access Control System using Convolutional Neural Networks. Research Journal of Applied Sciences, 13: 47-53.
  • Cengil, E., Çinar, A.,(2016).“A New Approach For Image Classification: Convolutional Neural Network”, European Journal of Technique (EJT), 6 (2), 96-103.
  • Chahar, H., & Nain, N. (2017, December). A Study on Deep Convolutional Neural Network Based Approaches for Person Re-identification. In International Conference on Pattern Recognition and Machine Intelligence (pp. 543-548). Springer, Cham.
  • Erdem, M. E., & Topal, C. (2018, May). Patch Warping Based Face Frontalization. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
  • Geng, M., Wang, Y., Xiang, T., Tian, Y. (2016). Deep Transfer Learning For Person Reidentification. arXiv preprint arXiv:1611.05244 .
  • Guo S., S. Chen and Y. Li. (2016). Face Recognition Based On Convolutional Neural Network And Support Vector Machine. IEEE International Conference on Information and Automation (ICIA), Ningbo, 2016, pp. 1787-1792. doi: 10.1109/ICInfA.2016.7832107.
  • İnik, Ö., Ülker, E., (2017). “Derin Öğrenme Ve Görüntü Analizinde Kullanılan Derin Öğrenme Modelleri”, Gaziosmanpaşa Bilimsel Araştırma Dergisi , 6 (3) , 85-104.
  • Kaplan, A. (2018). Gerçek ve Yarı Gerçek Zamanlı Yüz Tespit Etme/Face Detection On Real And Semi-Real Time. Fırat Üniversitesi Fen Bilimleri Enstitüsü, Yüksek Master Thesis. Elazığ.
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet Classification With Deep Convolutional Neural Networks. In Advances in neural information processing systems 25 (NIPS 2012) (pp. 1097-1105).
  • Li, W., Zhao, R., Xiao, T., Wang, X. (2014). Deepreid: deep filter pairing neural network for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 152–159). Liu, H., Feng, J., Qi, M., Jiang, J., Yan, S. (2016). End-to-end comparative attention networks for person re-identification, arXiv preprint arXiv:1606.04404.
  • Pala, T., Yücedağ, İ., Kahraman, H. T., Güvenç, U., & Sönmez, Y. (2018, September). Haar Wavelet Neural Network Model. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP) (pp. 1-8). IEEE.
  • Rashid, E. (2018). Raspberry Pi Ile Gerçek Zamanlı Yüz Tanıma Ve Kontrol Sistemi. Doctoral dissertation, Selçuk Üniversitesi Fen Bilimleri Enstitüsü. Konya.
  • Sharma, K. Shanmugasundaram and S. K. Ramasamy. (2016). FAREC — CNN based efficient face recognition technique using Dlib. International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), Ramanathapuram, 2016, pp. 192-195. doi: 10.1109/ICACCCT.2016.7831628.
  • Sharma, R., Ashwin, T. S., & Guddeti, R. M. R. (2019). A Novel Real-Time Face Detection System Using Modified Affine Transformation and Haar Cascades. In Recent Findings in Intelligent Computing Techniques (pp. 193-204). Springer, Singapore.
  • Su, C., Zhang, S., Xing, J., Gao, W., Tian, Q. (2016). Deep attributes driven multicamera Person re-identification. ECCV 2016. LNCS, vol. 9906, pp. 475–491. Springer, Cham Doi:10. 1007/978-3-319-46475-6 30.
  • Taşova, O., (2011). Yapay Sinir Ağları Ile Yüz Tanıma. Dokuz Eylül Üniversitesi Fen Bilimleri Enstitüsü Yüksek Lisans Tezi. İzmir.
  • Varior, R.R., Haloi, M., Wang, G. (2016). Gated Siamese convolutional neural network architecture for human re-identification. ECCV 2016. LNCS (vol. 9912, pp. 791–808). Springer, Cham Doi:10.1007/978-3-319-46484-8 48.
  • Vinay A. et al., (2017). G-CNN and F-CNN: Two CNN based architectures for face recognition. International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), Chirala (pp. 23-28). doi: 10.1109/ICBDACI.8070803
  • Wang, F., Zuo, W., Lin, L., Zhang, D., Zhang, L. (2016). Joint learning of single-image and cross-image representations for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1288–1296).
  • Wu, L., Shen, C., & van den Hengel, A. (2016). Convolutional LSTM networks for video-based person re-identification. arXiv preprint arXiv:1606.01609, 1(11).
  • Xiao, T., Li, H., Ouyang, W., Wang, X. (2016). Learning deep feature representations with domain guided dropout for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1249–1258).
  • Yaman, A. U., & Samet, R. D. (2018). Yüz tanıma sistemlerinin yanıltılmasına karşı bir yöntem: Yüz videolarında nabız tespiti ile canlılık doğrulaması, Ankara üniversitesi Fen Bilimleri Enstitüsü, Ankara.
  • Yang, M., David J. Kriegman, and Narendra Ahuja. (2002). “Detecting Faces in Images: A Survey”, IEEE Trans. Pattern Anal. Mach. Intell. 24, 1 (January 2002), 34–58. DOI:https://doi.org/10.1109/34.982883.
  • Yi, D., Lei, Z., Liao, S., Li, S.Z. (2014). Deep metric learning for person re-identification. In: Proceedings of International Conference on Pattern Recognition (pp. 2666–2672).
  • Zhang, Z., Si, T., & Liu, S. (2018). Integration convolutional neural network for person re-identification in camera networks. IEEE Access, 6, 36887-36896.
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Araştırma Makaleleri
Yazarlar

Faruk Ayata Bu kişi benim 0000-0003-2403-3192

Hayati Çavuş Bu kişi benim 0000-0001-5602-5221

Mevlüt İnan Bu kişi benim 0000-0002-9840-8404

Ebubekir Seyyarer Bu kişi benim 0000-0002-8981-0266

Emre Biçek Bu kişi benim 0000-0001-6061-9372

Erol Kına Bu kişi benim 0000-0002-7785-646X

Yayımlanma Tarihi 3 Mayıs 2020
Gönderilme Tarihi 20 Nisan 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 11 Sayı: 40

Kaynak Göster

APA Ayata, F., Çavuş, H., İnan, M., Seyyarer, E., vd. (2020). Dostroajan: Facial Recognition Based System Input Control Agent. AJIT-E: Academic Journal of Information Technology, 11(40), 82-96. https://doi.org/10.5824/ajite.2020.01.005.x
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