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

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.