Bulut Doğrulama Temelli Yüz Tanıma Tekniği

Year 2019, Volume: 3 Issue: 1, 79 - 96, 30.07.2019

Anfal Thaer Alrahlawee , Adil Deniz Duru , Oğuz Bayat Osman Nuri Uçan

Abstract

Öz

Tek bir yüz için tanıma süreci nispeten daha kısa sürede tamamlanabilir. Bununla birlikte, birkaç yüzün

tanınmasını içeren büyük ölçekli uygulama, prosedürü uzun bir hale getirecektir. Bulut bilişim hizmeti, daha

fazla veri işleneceği zaman bulut bilişimin temel kaynakları artırdığı bir ölçeklenebilirlik çözümü sağlaması

için bu araştırmada kullanılmıştır. Geliştirilen sistemin programlanması ve eğitimi, bulut bilişim yoluyla yüzleri

tespit etmek ve tanımak için yapılmıştır. İntegral görüntü, basamaklı sınıflandırıcılar, beş çeşit Haar benzeri

özellikler ve Adaboost öğrenme yöntemi kullanılan yüzleri tespit etmek için Viola ve Jones algoritması kullanılır.

Yüz tanıma, Temel Bileşen Analizi (PCA) algoritmasına göre daha verimli olduğu için Doğrusal Diskriminant

Analizi (LDA) kullanılarak yapılmıştır. Sistemin performansını değerlendirmek için çeşitli MUCT veritabanı

görüntüleri kullanılmıştır.

Keywords

Histogram eşitleme, Bulut bilgi işlem, Yüz algılama, Viola ve Jones algoritması, Doğrusal Diskriminant Analizi

Cloud Authentication Based Face Recognition Technique

Abstract

Abstract

The recognition process for a single face can be completed in relatively less time. However, large scale

implementation that involves recognition of several faces would make the procedure a lengthy one. Cloud

computing service has been employed in this paper to provide a solution for scalability, where cloud computing

increases the essential resources when larger data is to be processed. The programming and training of the

developed system has been done in order to detect and recognize faces through cloud computing. Viola and

Jones algorithm is employed for detecting faces that used integral image, cascaded classifiers, five sorts of

Haar-like features, and Adaboost learning method. Face recognition has been done using Linear Discriminant

Analysis (LDA), as it is more efficient compared to Principal Component Analysis (PCA) algorithm. Several

MUCT database images have been used for assessing the performance of system.

Keywords

Histogram equalization, Cloud computing, Face detection, Viola and Jones algorithm, Linear Discriminant Analysis

References

• Alireza S. 2013. ” Children Detection Algorithm Based on Statistical Models and LDA in Human Face Images”, Communication Systems and Network Technologies international conference.
• Alireza T. 2011. ” Face Detection and Recognition using Skin Color and AdaBoost Algorithm Combined with Gabor Features and SVM Classifier”, Multimedia and Signal Processing international conference.
• Avidan S. 2006. “Blind vision”, computer vision European conference.
• Brown, Michael PS, William Noble Grundy, David Lin, Nello Cristianini, Charles Walsh Sugnet, Terrence S. Furey, Manuel Ares, and David Haussler. 2000. “Knowledge-based analysis of microarray gene expression data by using support vector machines.” Proceedings of the National Academy of Sciences 97, no. 1: 262-267.
• Cheng M. 2015. “Global contrast based salient region detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence.
• Dufaux F. 2006. “Scrambling for Privacy Protection in Video Surveillance Systems”, IEEE transactions for video technology.
• Ganesh V. 2013. “STEP-2 User Authentication for Cloud Computing,” Engineering and Innovative Technology international journal.
• Hiroyuki K. 2007. ” Face Detection with Clustering, LDA and NN”, Systems, Man and Cybernetics international conference of IEEE.
• Ian F. 2008. “Cloud computing and grid computing 360 degree compared”, semantic scholar.
• J. Killoran. 2018. “4 Password Authentication Risk & How to Avoid Them”, End Passwords with Swoop. [Online]. Available: https://swoopnow.com/password-authentication/. [Accessed: 18- Oct- 2018].
• J. L. 2017. “Fingerprint Scanning: 5 Things to Know Before Implementing”, End Passwords with Swoop. [Online]. Available: https://swoopnow.com/fingerprint-scanning/. [Accessed: 18- Oct- 2018].
• Jagadish, H. 2014. “Big data and its technical challenges”, ACM communications magazine, volume 57. Jensen O. 2008. ”Implementing the Viola-Jones face detection algorithm”, PhD thesis, Denmark technical university.
• Jie Z. 2015. ” Real Time Face detection System Using Adaboost and Haar-like Features”, Information Science and Control Engineering international conference.
• Jindong W. 2016. ”Recognition using class specific linear Projection”, Chinese academy of science, institute of computing technology.
• K. Dharavath, F. Talukdar and R. Laskar. 2014. “Improving Face Recognition Rate with Image Preprocessing”, Indian Journal of Science and Technology, vol. 7, no. 8, pp. 1170–1175.
• Kalyani M. 2013. “Soft Computing on Medical-Data (SCOM) for a Countrywide Medical System using Data Mining and Cloud Computing Features”, technology and computer science international journal.
• Karthik K. 2010. “ Can offloading computation save energy”, Computer archive journal, Volume 43.
• Li, Cheng, and Bingyu Wang. 2014. “Fisher Linear Discriminant Analysis.” :1-6.
• Ming Y. 2010. ”PCA and LDA based fuzzy face recognition system”, SICE conference.
• N. Barnouti. 2016. “Improve Face Recognition Rate Using Different Image Pre-Processing Techniques”, American Journal of Engineering Research (AJER), vol. 5, no. 4, pp. 46-53.
• Neeti j. 2014. ” Analysis of Different Methods for Face Recognition”, Innovative Computer Science and Engineering International Journal, Vol. 1.
• Nilesh A. 2016. ”A review of authentication methods”, scientific and technology research international journal.
• O. Deniz, M. Castrillon, M. Hernandez. 2003. “Face recognition using independent component analysis and support vector machines”, Pattern Recognition Letters, vol. 24, pp. 2153-2157.43, 44, 45, 46, 47, 48, 49, 50.
• Ogbu R. 2013. “Cloud Computing and its Applications in e-Library Service”, Innovation, management and technology international journal.
• OneSpan. 2018. “Face Recognition Authentication”, Vasco.com. [Online]. Available: https://www.vasco. com/glossary/face-recognition-security.html. [Accessed: 18- Oct- 2018].
• Paul V. 2001. ” Rapid Object Detection using a Boosted Cascade of Simple Features”, computer vision and pattern recognition conference.
• Paula C. 2009. “Compression independent reversible encryption for privacy in video surveillance”, information security journal, Vol. 2009.
• Peter M. 2011. “The Nist Definition of Cloud Computing,” standards and technology national institute.
• Phillips P. 2010. “Face Recognition Vendor Test”, IEEE transactions.
• Puja D. 2012. “Cloud computing’s and its applications in the world of networking”, Computer Science international journal, Vol. 9.
• Rajesh P. 2012. “An Overview and Study of Security Issues & Challenges in Cloud Computing,”, Advanced Research in Computer Science and Software Engineering international journal.
• Roger L. 2010. “Fundamentals of Digital Image Processing”, Prentice-Hall, first edition.
• Rupali S. 2017. ” A Real Sense based multilevel security in cloud framework using Face recognition and Image processing”, Convergence in Technology international conference.
• Scott H. 2000. “Techniques for addressing fundamental privacy and distribution tradeoffs in awareness support systems”, computer supported ACM conference.
• Senthil P. 2012. “Improving the Security of Cloud Computing using Trusted Computing Technology,” Modern Engineering Research international journal.
• Stan Z. 2011. “Handbook of face recognition”, Springer verlag London, second edition, ISBN 978-0-85729- 931-4.
• Stan Z. 2011. ” Handbook of Face Recognition”, Springer, second edition, ISBN 978-0-85729-931-4.
• Sung H. 2010. ” Face/Non-face Classification Method Based on Partial Face Classifier Using LDA and MLP”, Computer and Information Science international conference of IEEE.
• V. Blanz and T. Vetter. 2003. “Face recognition based on fitting a 3D morphable model”, IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 25, no. 9, September.
• Younis A. 2013. “Secure cloud computing for critical infrastructure”, Liverpool John Moore’s University, United Kingdom.
• Zahid M. 2012. ” Automatic Player Detection and Recognition in Images Using AdaBoost”, Electrical Engineering, Mathematics & Computer Science faculty, Twente university.