Decision Fusion for Multimodal Biometrics Using Social Network Analysis

Paul, Padma Polash; Gavrilova, Marina L.; Alhajj, Reda

doi:10.1109/tsmc.2014.2331920

Cited by 65 publications

(26 citation statements)

References 33 publications

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“…In addition to the coarse categories above, several levels of the biometric processing pipeline can be distinguished where information fusion can be performed (see e.g. Ross et al [32]): [43]. In the context of this work, information fusion on score and rank level is of most interest.…”

Section: Biometric Information Fusionmentioning

confidence: 99%

Multi-Biometric Identification With Cascading Database Filtering

Drozdowski

Rathgeb

Mokros

et al. 2020

IEEE Trans. Biom. Behav. Identity Sci.

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The growing scale and number of biometric deployments around the world necessitates research into technologies which facilitate fast identification queries and high discriminative power. In this context, this article presents a biometric identification system which relies on a successive pre-filtering of the potential candidate list using multiple biometric modalities, coupled with a weighted score-level information fusion. The proposed system is evaluated in a series of experiments using a compound dataset constructed from several publicly available datasets; an open-set identification scenario is considered with the enrolment database containing 1,000 chimeric instances. This evaluation shows that the proposed system exhibits a significantly increased biometric performance w.r.t. a weighted score-level or rank-level fusion based baseline, while simultaneously providing a consequential computational workload reduction in terms of penetration rate. Lastly, it is worth noting that the proposed system could be flexibly employed in any multi-biometric identification system, irrespective of the chosen types of biometric characteristics and the encoding of their extracted features.

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Section: Biometric Information Fusionmentioning

confidence: 99%

Multi-Biometric Identification With Cascading Database Filtering

Drozdowski

Rathgeb

Mokros

et al. 2020

IEEE Trans. Biom. Behav. Identity Sci.

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“…Hybrid multimodal system [143] fused face, ear, and signature with social network analysis at the decision level to enhance the biometric recognition performance.…”

Section: Unimodal and Multimodal Authentication Systemsmentioning

confidence: 99%

Demystifying Authentication Concepts in Smartphones: Ways and Types to Secure Access

Gupta

Buriro

Crispo

2018

Mobile Information Systems

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Smartphones are the most popular and widespread personal devices. Apart from their conventional use, that is, calling and texting, they have also been used to perform multiple security sensitive activities, such as online banking and shopping, social networking, taking pictures, and e-mailing. On a positive side, smartphones have improved the quality of life by providing multiple services that users desire, for example, anytime-anywhere computing. However, on the other side, they also pose security and privacy threats to the users' stored data. User authentication is the first line of defense to prevent unauthorized access to the smartphone. Several authentication schemes have been proposed over the years; however, their presentation might be perplexing to the new researchers to this domain, under the shade of several buzzwords, for example, active, continuous, implicit, static, and transparent, being introduced in academic papers without comprehensive description. Moreover, most of the reported authentication solutions were evaluated mainly in terms of accuracy, overlooking a very important aspect-the usability. is paper surveys various types and ways of authentication, designed and developed primarily to secure the access to smartphones and attempts to clarify correlated buzzwords, with the motivation to assist new researchers in understanding the gist behind those concepts. We also present the assessment of existing user authentication schemes exhibiting their security and usability issues.

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“…A20 Classifying ecommerce information sharing behaviour by youths on social networking sites 2011 [38] A21 Clustering memes in social media 2013 [39] A22 Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation 2010 [40] A23 Collaborative visual modeling for automatic image annotation via sparse model coding 2012 [41] A24 Confucius and its intelligent disciples: integrating social with search 2010 [42] A25 Content Feature Enrichment for Analyzing Trust Relationships in Web Forums 2013 [43] A26 Content Matters : A study of hate groups detection based on social networks analysis and web mining 2013 [44] A27 Co-training over Domain-independent and Domain-dependent features for sentiment analysis of an online cancer support community 2013 [45] A28 Data-Mining Twitter and the Autism Spectrum Disorder : A Pilot Study 2014 [46] A29 Decision Fusion for Multimodal Biometrics Using Social Network Analysis 2014 [47] A30 Detecting Deception in Online Social Networks 2014 [48] A31 Enhancing financial performance with social media: An impression management perspective 2013 [49] A32 Enriching short text representation in microblog for clustering 2012 [50] A33 Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics 2011 [51] A34 A56 The potential of social media in delivering transport policy goals 2014 [74] A57 The social media genome: modeling individual topic-specific behavior in social media 2013 [75] A58 Topic-sensitive influencer mining in interest-based social media networks via hypergraph learning 2014 [76] A59 Twitter, MySpace, Digg: Unsupervised Sentiment Analysis in Social Media 2012 [77] A60 Unsupervised and supervised learning to evaluate event relatedness based on content mining from socialmedia streams 2012 [78] A61 Using explicit linguistic expressions of preference in social media to predict voting behavior 2013 [79] A62 Using inter-comment similarity for comment spam detection in Chinese blogs 2011 [80] A63 Using Sentiment to Detect Bots on Twitter: Are Humans more Opinionated than Bots? 2014 [81] A64 Using social media to enhance emergency situation awareness 2012 [82] A65 Web data extraction, applications and techniques: A survey 2014 [83] A66 What's in twitter: I know what part...…”

Section: A40mentioning

confidence: 99%