Abstract
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.
Highlights
T HE decision support system analyzes data to make various types of predictions
WORKS This research analyzes the individual performance of different Social Behavioral Biometric (SBB) traits as well as integrates a new trait with the existing SBB traits to generate a higher accurate SBB system
This is the first study that investigates the impacts of score level fusion and rank level fusion algorithms on Social Behavioral Biometrics
Summary
T HE decision support system analyzes data to make various types of predictions. Physical and behavioral biometric traits can be used to support such system. An intelligent biometric design support system was proposed as part of the Physical Access Control System in [1]. Online Social Networks (OSN) data is very helpful to augment the domain of decision support systems. Twitterdriven analytics is used in a crime predictive decision support system [2]; users’ web browsing behaviors are used in user identification [3], etc. The artificial intelligence approaches, such as cognitive systems, fuzzy logic, genetic algorithms, intelligent agents, case-based reasoning, evolutionary computing and artificial neural networks (ANN) can be incorporated with traditional decision support systems to augment their capabilities and improve their adaptation [4]–[8]. Social behavioral features can be used to develop socially intelligent and cognitive robots, which improve the interaction between humans and robots [9], [10]
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