A Multi-sample Multi-source Model for Biometric Authentication

In this study, two techniques that can improve the authentication process are examined: (i) multiple samples and (ii) multiple biometric sources. We propose the fusion of multiple samples obtained from multiple biometric sources at the score level. By using the average operator, both the theoretical and empirical results show that integrating as many samples and as many biometric sources as possible can improve the overall reliability of the system. This strategy is called multi-sample multi-source approach. This strategy was tested on a real-life database using neural networks trained in one-versus-all configuration

A Multi-sample Multi-source Model for Biometric Authentication

In this study, two techniques that can improve the authentication process are examined: (i) multiple samples and (ii) multiple biometric sources. We propose the fusion of multiple samples obtained from multiple biometric sources at the score level. By using the average operator, both the theoretical and empirical results show that integrating as many samples and as many biometric sources as possible can improve the overall reliability of the system. This strategy is called multi-sample multi-source approach. This strategy was tested on a real-life database using neural networks trained in one-versus-all configuration

Actor-network procedures: Modeling multi-factor authentication, device pairing, social interactions

As computation spreads from computers to networks of computers, and migrates into cyberspace, it ceases to be globally programmable, but it remains programmable indirectly: network computations cannot be controlled, but they can be steered by local constraints on network nodes. The tasks of "programming" global behaviors through local constraints belong to the area of security. The "program particles" that assure that a system of local interactions leads towards some desired global goals are called security protocols. As computation spreads beyond cyberspace, into physical and social spaces, new security tasks and problems arise. As networks are extended by physical sensors and controllers, including the humans, and interlaced with social networks, the engineering concepts and techniques of computer security blend with the social processes of security. These new connectors for computational and social software require a new "discipline of programming" of global behaviors through local constraints. Since the new discipline seems to be emerging from a combination of established models of security protocols with older methods of procedural programming, we use the name procedures for these new connectors, that generalize protocols. In the present paper we propose actor-networks as a formal model of computation in heterogenous networks of computers, humans and their devices; and we introduce Procedure Derivation Logic (PDL) as a framework for reasoning about security in actor-networks. On the way, we survey the guiding ideas of Protocol Derivation Logic (also PDL) that evolved through our work in security in last 10 years. Both formalisms are geared towards graphic reasoning and tool support. We illustrate their workings by analysing a popular form of two-factor authentication, and a multi-channel device pairing procedure, devised for this occasion.Comment: 32 pages, 12 figures, 3 tables; journal submission; extended references, added discussio

Multimodal person recognition for human-vehicle interaction

Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies

Genetic Programming for Multibiometrics

Biometric systems suffer from some drawbacks: a biometric system can provide in general good performances except with some individuals as its performance depends highly on the quality of the capture. One solution to solve some of these problems is to use multibiometrics where different biometric systems are combined together (multiple captures of the same biometric modality, multiple feature extraction algorithms, multiple biometric modalities...). In this paper, we are interested in score level fusion functions application (i.e., we use a multibiometric authentication scheme which accept or deny the claimant for using an application). In the state of the art, the weighted sum of scores (which is a linear classifier) and the use of an SVM (which is a non linear classifier) provided by different biometric systems provide one of the best performances. We present a new method based on the use of genetic programming giving similar or better performances (depending on the complexity of the database). We derive a score fusion function by assembling some classical primitives functions (+, *, -, ...). We have validated the proposed method on three significant biometric benchmark datasets from the state of the art

Multi-biometric templates using fingerprint and voice

As biometrics gains popularity, there is an increasing concern about privacy and misuse of biometric data held in central repositories. Furthermore, biometric verification systems face challenges arising from noise and intra-class variations. To tackle both problems, a multimodal biometric verification system combining fingerprint and voice modalities is proposed. The system combines the two modalities at the template level, using multibiometric templates. The fusion of fingerprint and voice data successfully diminishes privacy concerns by hiding the minutiae points from the fingerprint, among the artificial points generated by the features obtained from the spoken utterance of the speaker. Equal error rates are observed to be under 2% for the system where 600 utterances from 30 people have been processed and fused with a database of 400 fingerprints from 200 individuals. Accuracy is increased compared to the previous results for voice verification over the same speaker database

Authentication of Students and Students’ Work in E-Learning : Report for the Development Bid of Academic Year 2010/11

Global e-learning market is projected to reach $107.3 billion by 2015 according to a new report by The Global Industry Analyst (Analyst 2010). The popularity and growth of the online programmes within the School of Computer Science obviously is in line with this projection. However, also on the rise are students’ dishonesty and cheating in the open and virtual environment of e-learning courses (Shepherd 2008). Institutions offering e-learning programmes are facing the challenges of deterring and detecting these misbehaviours by introducing security mechanisms to the current e-learning platforms. In particular, authenticating that a registered student indeed takes an online assessment, e.g., an exam or a coursework, is essential for the institutions to give the credit to the correct candidate. Authenticating a student is to ensure that a student is indeed who he says he is. Authenticating a student’s work goes one step further to ensure that an authenticated student indeed does the submitted work himself. This report is to investigate and compare current possible techniques and solutions for authenticating distance learning student and/or their work remotely for the elearning programmes. The report also aims to recommend some solutions that fit with UH StudyNet platform.Submitted Versio