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     Research Journal of Applied Sciences, Engineering and Technology


Accessing Social Network Sites Using Work Smartphone for Face Recognition and Authentication

1Ahmad M. Al Smadi, 2Mutasem K. Alsmadi, 3Hussein Al Bazar, 4Saleh Alrashed and 1Bushra S. Al Smadi
1Department of Computer Science, Al-Balqa Applied University, Ajloun College University, Jordan
2Department of MIS, Collage of Applied Studies and Community Service, University of Dammam, KSA
3Faculty of Computer Studies, Arab Open University, KSA
4College of Computer Science and Information Technology, University of Dammam, KSA
Research Journal of Applied Sciences, Engineering and Technology  2015  1:56-62
http://dx.doi.org/10.19026/rjaset.11.1675  |  © The Author(s) 2015
Received: February ‎3, ‎2015  |  Accepted: March ‎1, ‎2015  |  Published: September 05, 2015

Abstract

Nowadays, Social Networking Sites (SNS) are increasingly getting the attention of academic, industrial researchers intrigued by their affordances and gradually gaining its importance and became a major method used to share thoughts, video, image, etc., in various domains such as research, politics, religion, academics and development. Apart of its strength points SNS has one major drawback which is the inefficient authentication of users to login. Due to this drawback; different types of fake message, non-social activities, national or personal threats, Numbers, videos and other important things are used for extortion people, which can be posted by some imposters or non-social personals. In spite of the importance of authentication in the social network, a handful number of researches conducted such accessing social network using efficient authentication technique to solve this problem. This study proposed a method to access a social network sites (such as Facebook and twitter) using face recognition techniques at the time of login in the site by Smartphones. Where Local Binary Pattern (LBP) was used to detect users face and the LBP histogram was used for features extraction. The proposed method obtained very promising results in term of accuracy (93.5%) and effectiveness for authentication of user identity.

Keywords:

Authentication, biometrics, face recognition, feature extraction, LBP histogram, Local Binary Pattern (LBP), security, social network site,


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Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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