Madeena Sultana

A Concept of Social Behavioral Biometrics: Motivation, Current Developments, and Future Trends

A person can be identified from his physiological traits as well as from behavioral patterns. However, a persons behavior is not only confined to individual actions such as walking or typing style, speech or handwriting but also social interactions and communication. In other words, social communication is an indispensable part of our daily behavior. Therefore, a persons social connections, spatio-temporal information, style of interactions etc. Can be a good source of information to identify his social behavioral pattern. Based on this hypothesis, this paper introduces a novel kind of behavioral biometrics called Social Behavioral Biometrics (SBB) for the first time. The study includes identification of social behavioral biometric features from real and virtual domain and their prospective applications for the purpose of person authentication and verification.

Social Behavioral Biometrics: An Emerging Trend

In todays world, identity of human beings has expanded beyond the real world to the cyber world. Virtual identity of millions of users is present at various web-based Social Networking Sites (SNSs) such as Myspace, Facebook, and Twitter. Interactions through SNSs have become a part of our daily practices, which eventually leaves a big trail of behavioral pattern in virtual domain. In this paper, the authors examined the feasibility of person identification using such social network activities as behavioral biometrics. Experimentation includes extraction of a number of idiosyncratic features from SNSs and analysis of their performance as novel social behavioral biometric features.

Social Behavioral Information Fusion in Multimodal Biometrics

The goal of a biometric recognition system is to make a human-like decisions on individual’s identity by recognizing their physiological and/or behavioral traits. Nevertheless, the decision-making process by either a human or a biometric recognition system can be highly complicated due to low quality of data or an uncertain environment. Human brain has an advantage over computer system due to its ability to perform a massive parallel processing of auxiliary information, such as visual cues, cognitive and social interactions, contextual, and spatio-temporal data. Similarly to a human brain, social behavioral cues can aid the reliable decision-making of an automated biometric system. In this paper, a novel person recognition approach is presented, that relies on the knowledge of individuals’ social behavior to enhance the performance of a traditional biometric system. The social behavioral information of individuals’ has been mined from an online social network and fused with traditional face and ear biometrics. Experimental results on individual’s and semi-real databases demonstrate significant performance gain in the proposed method over traditional biometric system.

Mining Social Behavioral Biometrics in Twitter

Online Social Networking Sites (SNSs) are considered as one of the well-established mediums of mass communication in todays world. Similar to physical world humans tend to have unique pattern of social communication in virtual world. However, analysis of such web based communication patterns is rarely seen for person identification. Most of the existing biometric recognition systems use either individual physiological or behavioural traits. A framework for the analysis of the web-based social interaction data as biometric features is largely unexplored until now. In this paper, a framework to accumulate and analyze social communication based data from online SNSs is presented. Analysis of such features explores personal characteristics, knowledge, and communication patterns that can successfully be utilized as Social Behavioral Biometric features. Experimental results demonstrate that the proposed social behavioral biometric features are significantly useful for person authentication.

Editing Behavior to Recognize Authors of Crowdsourced Content

During this era of internet, crowd-sourcing is a very popular way of accommodating a large group of people contributing together to accomplish a goal. One of the most remarkable examples of such crowd sourced content is the Wikipedia, where millions of articles have been produced by volunteers from all over the world. Wikipedia allows anyone to edit articles without being authorized. Although creation of this huge repository of information is being possible because of the freedom of editing, it also attracts sock puppets and malicious users to cause ruthless destruction in Wikipedia contents. One way of dealing with such malevolent users is to predict the identity of ambiguous authors. However, authorship recognition in collaborative environment like Wikipedia is very challenging. In this paper, we propose a novel way of mapping ambiguous users identity to previously known users based on their editing profile. The proposed editing behavior based authorship recognition can be applied to decide on trusty and offensive authors, identity theft, shock puppetry, human behavior analysis, and so on. Our experimentation on a large database of Wikipedia demonstrate promising results of using editing behavior to recognize authors of collaborative writing.

Adaptive multi-stream score fusion for illumination invariant face recognition

Quality variations of samples significantly affect the performance of biometric recognition systems. In case of face recognition systems, illumination degradation is the most common contributor of enormous intra-class variation. Wavelet transforms are very popular techniques for face or object recognition from images due to their illumination insensitiveness. However, low and high frequency subbands of wavelet transforms do not possess equal insensitiveness to different degree of illumination change. In this paper, we investigated the illumination insensitiveness of the subbands of Dual-Tree Complex Wavelet Transform (DTCWT) at different scales. Based on the investigations, a novel face recognition system has been proposed using weighted fusion of low and high frequency subbands that can adapt extensive illumination variations and produces high recognition rate even with a single sample. A novel fuzzy weighting scheme has been proposed to determine the adaptive weights during uncertain illuminations conditions. In addition, an adaptive normalization approach has been applied for illumination quality enhancement of the poor lit samples while retaining the quality of good samples. The performance of the proposed adaptive method has been evaluated on Extended Yale B and AR face databases. Experimental results exhibit significant performance improvement of the proposed adaptive face recognition approach over benchmark methods under extensive illumination change.

User Recognition From Social Behavior in Computer-Mediated Social Context

Social interactions are integral part of human behavior. Although social interactions are likely to possess unique behavioral patterns, their significance for automated user recognition has been noted in the scientific community only recently. This paper demonstrated that it is possible to generate a set of unique features, called social behavioral (SB) features, from the social interactions of individuals’ via an online social network (OSN). Specifically, this research identified a set of SB features from the online social interactions of 241 Twitter users and proposed a framework to utilize these features for an automated user recognition. Extensive experimentation demonstrated high recognition performance as well as distinctiveness of the proposed SB features. The most striking finding was that only ten recent tweets are enough to recognize 58% of users in our database at rank-1. The rank-1 recognition rate dramatically increased to 93% when 60 tweets were used as a probe set. Experimental results also demonstrated the stability of the proposed SB feature set over time and ability to recognize both frequent and nonfrequent OSN users. This confirms that human social behavior expressed through an OSN can provide a unique insight into user behavior recognition.

High density impulse denoising by a novel adaptive fuzzy filter

Traditional median filters perform well in restoring the images corrupted by low density impulse noise, but fail to restore highly corrupted images. Conversely, the advanced adaptive median filters are capable of denoising high density impulse noise but the image details are compromised significantly. In this paper, a new adaptive fuzzy median filter is presented to provide optimum detail preservation along with very high density noise removal. The novelty of this research work comes from two directions. Firstly, we used a triangular fuzzy membership function to determine the level of corruption at each pixel that consequently ensures the replacement of noisy pixels according to the extent of corruption. Secondly, we exploited fully adaptive and automatically adjustable threshold value to provide ease of computation. Experimental results show that the proposed filter outperforms other conventional and advanced filters in terms of both denoising and fine detail preservation of highly corrupted images.

Face recognition using multiple content-based image features for biometric security applications

During the era of internet, content-based image retrieval (CBIR) systems, where images are searched based on their visual contents, have an increasing demand for numerous real world applications. However, the potential of using multiple CBIR-based features for biometric recognition remains largely unexplored. This research presents an in-depth analysis of current research trends of CBIR and its potential applications in the field of biometric security. A novel content-based face recognition system is proposed and experimental results are provided to strengthen the material of this article. In the proposed face recognition system, three content-based low level features: colour, texture, and shape are combined to enhance the recognition accuracy. Moreover, the simplicity and ease of computation of the exploited methods reduce computation time. Experimental results show that the proposed multiple low level feature-based method outperforms single feature-based face recognition systems.

Fuzzy Rule Based Quality Measures for Adaptive Multimodal Biometric Fusion at Operation Time

Sample quality variation at operation time is one of the major concerns of real time biometric authentication and surveillance systems. Quality deviations of samples affect the performance of many benchmark biometric trait recognition systems. Moreover, large variation between enrolled and probe samples is very uncertain since it may arise at operation time for various reasons. In this paper, a novel adaptive multimodal biometric system is presented that can adapt the uncertainty of the quality degradation during operation. Fuzzy rule based method is applied for the first time to calculate the quality score of template-probe pairs dynamically. Feature extraction is accomplished using a novel shift invariant multi-resolution fusion approach. Finally, face and ear modalities are fused adaptively at rank level based on the quality scores. Proposed method relies more on good quality samples and disregards misclassification of poor quality samples. Experimental results demonstrate significant performance improvement of the proposed adaptive multimodal approach over baseline i.e. non-adaptive method.