FACE VERIFICATION SYSTEM IN MOBILE DEVICES BY USING COGNITIVE SERVICES

Adem Alpaslan Altun, Cagatay Kolus
  • Cagatay Kolus
    Affiliation not present

Abstract

Biometric systems enable people to distinguish between physical and behavioral characteristics. Face recognition systems, a type of biometric systems, use peoples’ facial features to recognize them. The aim of this study is to perform face recognition and verification system that can run on mobile devices. The developed application is based on comparing the faces in two photographs. The user uploads two photos to the system, the system identifies the faces in these photos and performs authentication between the two faces. As a result, the system gives the output that the two faces in the photo belong to the same or different persons. It provides a security measure thanks to the face identification and verification feature included in this application. This application can be integrated into various applications and used in systems such as user login.

Keywords

Cognitive services, Face identification, Face verification, Image Processing, Mobile application

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Submitted: 2018-08-06 08:35:25
Published: 2018-12-27 18:57:41
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References

F. C. Elbizim, M. C. Kasapbasi, (2017) Implementation and Evaluation of Face Recognition Based Identification System, International Journal of Intelligent Systems and Applications in Engineering, pp. 17-20

A. Eleyan, (2017) Simple and Novel Approach for Image Representation with Application to Face Recognition, International Journal of Intelligent Systems and Applications in Engineering, vol. 5(3), pp. 89-93.

S. Nikan, M. Ahmadi, (2015) Performance Evaluation of Different Feature Extractors and Classifiers for Recognition of Human Faces with Low Resolution Images, International Journal of Intelligent Systems and Applications in Engineering, vol. 3(2), pp. 72-77.

W. Zhao, R. Chellappa, P. J. Phillips and A. Rosenfeld, 2003, “Face Recognition: A Literature Survey”, ACM Computing. Surveys; 35(4): 399–458

J. A. Lee, A. Szabo, Y. Li, (2013) AIR: An Agent for Robust Image Matching and Retrieval, International Journal of Intelligent Systems and Applications in Engineering, vol. 1(2), pp. 34-39.

H. Mliki, E. Fendri, M. Hammami, 2015, “Face Recognition Through Different Facial Expressions”, Journal of Signal Processing Systems, vol 81(3), pp. 433-446.

N. Delbiaggio, A comparison of facial recognition’s algorithms, Bachelor's Degree, Haaga-Helia, 2017

E. Sütçüler, 2006, “Real-Time Face Localization and Recognition System by Using Video Sequences”, Msc Thesis, Yıldız Technical University.

P. Kirci, G. Kurt, (2016) Long Term and Remote Health Monitoring with Smart Phones, International Journal of Intelligent Systems and Applications in Engineering, vol. 4(4), pp. 80-83.

G. Eryigit, G. Celikkaya, (2017) Use of NLP Techniques for an Enhanced Mobile Personal Assistant: The Case of Turkish, International Journal of Intelligent Systems and Applications in Engineering, vol. 5(3), pp. 94-104.

Beginning Android Programming with Android Studio, John Wiley & Sons, 2016, ISBN: 1118707427, 9781118707425

A. Del Sole, Introducing Microsoft Cognitive Services, In: Microsoft Computer Vision APIs Distilled. Apress, Berkeley, CA, https://doi.org/10.1007/978-1-4842-3342-9_1

Face Verification, https://azure.microsoft.com/tr-tr/services/cognitive-services/face [Access Date: 05.01.2018]

Genymotion User Guide – Version 2.11 https://docs.genymotion.com/pdf/PDF_User_Guide/Genymotion-2.11-User-Guide.pdf [Access Date: 06.01.2018]

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