Score and Rank Level Fusion Algorithms for Social Behavioral Biometrics

The goal of a biometric system is to recognize individuals based on their unique physiological or behavioral traits. Online Social Networking (OSN) platforms have become an integral part of the daily life of individuals, where they leave a recognizable trail of behavioral information. Social Behavioral Biometric (SBB), being an emerging trend, focuses on such trails to distinguish between individuals. This research investigates the impact of users’ writing profiles on OSN to conclude whether such profiles contribute to SBB. The distinctiveness of the SBB features that are extracted from the social behavioral data of Twitter is studied. A person identification system that relies on users’ writing profiles, reply, retweet, shared weblink, trendy topic networks and temporal profiles is proposed. Score and rank level weighted fusion algorithm performance is compared on a social interaction database of 241 Twitter users. The experimental results establish that the users’ writing profiles have the highest impact over other social biometric features and that score level fusion algorithms perform better than rank level fusion on SBB. The proposed system has achieved recognition rate of 99.45% at rank-1 after cross-validation using genetic algorithm based score level fusion algorithm. The system outperformed all prior researches on SBB in terms of identification accuracy.