Cost-Effective and Scalable Image Matching Across Heterogeneous Online Social Networks

R. Devakunchari 1 *, C. Valliyammai 1
More Detail
1 Anna University, Chennai, India
* Corresponding Author
Online Journal of Communication and Media Technologies, Volume 6, Issue December 2016 - Special Issue, pp. 258-271. https://doi.org/10.30935/ojcmt/5657
OPEN ACCESS   1177 Views   697 Downloads   Published online: 01 Dec 2016
Download Full Text (PDF)

ABSTRACT

In this modern era, social media facilitates us in communicating with the people across the world. Sharing of photos on social networks is due to their addictive interest in receiving the likes from other users in the network to gain popularity. Users on social network upload nearly 1.8 billion photos every day. Malicious users try to gather the publicly available photos on social networks and use it for creating bogus accounts. To identify those anomalous users, the photos shared are collected and processed by the social network manager to classify the original person from the fake one. As there are billions of users in each social network, there are an enormous amount of photo uploads which leads to the problem of scalability, slower processing performance and execution speed. The primary objective of this paper is to identify the similar image for a given a query image on a large set of image datasets crawled from online social networks through the Internet. For handling the scalability incurred from large image data sets, the image matching computation is implemented in distributed computing Map Reduce framework. The face recognition involves supervised machine learning approach employing Computer vision algorithms, namely Fisher face, Eigenface and Local Binary Pattern Histogram (LBPH). The similarity functions are used to calculate the distance between the query image and image sequence in the Hadoop file system. Experiments use the trained data sets to find the least similarity measure. The results obtained show that LBPH provides better and accurate matching results compared to other two face recognition approaches.

CITATION

Devakunchari, R., & Valliyammai, C. (2016). Cost-Effective and Scalable Image Matching Across Heterogeneous Online Social Networks. Online Journal of Communication and Media Technologies, 6(December 2016 - Special Issue), 258-271. https://doi.org/10.30935/ojcmt/5657

REFERENCES

  • Ding, C., Xu, C., & Tao, D. (2015). Multi-task pose-invariant face recognition. IEEE Transactions on Image Processing, 24(3), 980-993. doi: 10.1109/TIP.2015.2390959.
  • Face Recognition with OpenCV. Retrieved February 20, 2016, from http://docs.opencv.org/2.4/modules/contrib/doc/facerec/facerec_tutorial.html#fisherfaces
  • Fang, Q., Sang, J., Xu, C., & Hossain, M. S. (2015). Relational user attribute inference in social media. IEEE Transactions on Multimedia, 17(7), 1031-1044.
  • Gao, S., Zhang, Y., Jia, K., Lu, J., & Zhang, Y. (2015). Single sample face recognition via learning deep supervised autoencoders. IEEE Transactions on Information Forensics and Security, 10(10), 2108-2118. doi: 10.1109/TIFS.2015.2446438
  • Guo, L., Zhang, C., & Fang, Y. (2015). A trust-based privacy-preserving friend recommendation scheme for online social networks. IEEE Transactions on Dependable and Secure Computing, 12(4), 413-427.
  • Huang, Z., Shan, S., Wang, R., Zhang, H., Lao, S., Kuerban, A., & Chen, X. (2015). A benchmark and comparative study of video-based face recognition on COX face database. IEEE Transactions on Image Processing, 24(12), 5967-5981. doi: 10.1109/TIP.2015.2493448
  • Liang, X., Li, X., Zhang, K., Lu, R., Lin, X., & Shen, X. S. (2013). Fully anonymous profile matching in mobile social networks. IEEE Journal on Selected Areas in Communications, 31(9), 641-655.
  • Najaflou, Y., Jedari, B., Xia, F., Yang, L. T., & Obaidat, M. S. (2015). Safety challenges and solutions in mobile social networks. IEEE Systems Journal, 9(3), 834-854.
  • Nguyen, H. T., & Caplier, A. (2015). Local patterns of gradients for face recognition. IEEE Transactions on Information Forensics and Security, 10(8), 1739-1751. doi: 10.1109/TIFS.2015.2426144.
  • Squicciarini, A. C., Lin, D., Sundareswaran, S., & Wede, J. (2015). Privacy policy inference of user-uploaded images on content sharing sites. IEEE transactions on knowledge and data engineering, 27(1), 193-206.
  • Welcome to Apache Hadoop! Retrieved February 20, 2014, from http://hadoop.apache.org/
  • Welcome to Apache Pig! Retrieved January 2, 2013, from https://pig.apache.org/
  • Xia, F., Liu, L., Li, J., Ma, J., & Vasilakos, A. V. (2015). Socially aware networking: A survey. IEEE Systems Journal, 9(3), 904-921.
  • Yale Face Database, Retrieved Jan. 18, 2016, from http://vision.ucsd.edu/content/yale-face-database
  • Zhao, W., Chellappa, R., Phillips, P., and Rosenfeld, (2003). A. Face recognition: A literature survey. ACM Computing Surveys (CSUR) 35(4), 399–458.