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

Fast computation of the performance evaluation of biometric systems: application to multibiometric

The performance evaluation of biometric systems is a crucial step when designing and evaluating such systems. The evaluation process uses the Equal Error Rate (EER) metric proposed by the International Organization for Standardization (ISO/IEC). The EER metric is a powerful metric which allows easily comparing and evaluating biometric systems. However, the computation time of the EER is, most of the time, very intensive. In this paper, we propose a fast method which computes an approximated value of the EER. We illustrate the benefit of the proposed method on two applications: the computing of non parametric confidence intervals and the use of genetic algorithms to compute the parameters of fusion functions. Experimental results show the superiority of the proposed EER approximation method in term of computing time, and the interest of its use to reduce the learning of parameters with genetic algorithms. The proposed method opens new perspectives for the development of secure multibiometrics systems by speeding up their computation time.Comment: Future Generation Computer Systems (2012

A Swarm intelligence approach for biometrics verification and identification

In this paper we investigate a swarm intelligence classification approach for both biometrics verification and identification problems. We model the problem by representing biometric templates as ants, grouped in colonies representing the clients of a biometrics authentication system. The biometric template classification process is modeled as the aggregation of ants to colonies. When test input data is captured -- a new ant in our representation -- it will be influenced by the deposited phermonones related to the population of the colonies. We experiment with the Aggregation Pheromone density based Classifier (APC), and our results show that APC outperforms ``traditional'' techniques -- like 1-nearest-neighbour and Support Vector Machines -- and we also show that performance of APC are comparable to several state of the art face verification algorithms. The results here presented let us conclude that swarm intelligence approaches represent a very promising direction for further investigations for biometrics verification and identification

Initial development of a learners’ ratified acceptance of multibiometrics intentions model (RAMIM)

Authenticating users is a continuous tradeoff between the level of invasiveness and the degree of system security. Password protection has been the most widely authentication approach used, however, it is easily compromised. Biometric authentication devices have been implemented as a more robust approach. This paper reports on initial results of student perceptions about their acceptance of a multibiometrics authentication approach in the context of e-learning systems. Specifically, this paper reports on the initial empirical development of a learners’ Ratified Acceptance of Multibiometrics Intentions Model (RAMIM). The model proposed investigates the impact of students’ code of conduct awareness, perceived ease-of-use, perceived usefulness, and ethical decision making on learners’ intention to use multibiometrics for authentication during elearning exams. The study’s participants included 97 non-information technology (IT) students who attended e-learning courses. Additionally, results of a path analysis using Partial Least Square (PLS) indicate that perceived usefulness has the most significant impact on learners’ intention to use multibiometrics during e-learning exams. Students’ ethical decision making and perceived usefulness demonstrated significant impact on their intention to use multibiometrics. Additionally, students’ code of conduct awareness appears to have a positive impact on their ethical decision making. Conclusions are discussed including recommendations for future research on extending this initial research into applied experiments to address e-learning security issues

Automated border control systems: biometric challenges and research trends

Automated Border Control (ABC) systems automatically verify the travelers\u2019 identity using their biometric information, without the need of a manual check, by comparing the data stored in the electronic document (e.g., the e-Passport) with a live sample captured during the crossing of the border. In this paper, the hardware and software components of the biometric systems used in ABC systems are described, along with the latest challenges and research trends

Investigating the Role of Multibiometric Authentication in Professional Certification E-exams

E-learning has grown to such an extent that paper-based testing is being replaced by computer-based testing also known as e-exams. Because these e-exams can be delivered outside of the traditional proctored environment, additional authentication measures must be employed in order to offer similar authentication assurance as found in proctored, Paper-Based Testing (PBT). In this study, we extended the body of knowledge in e-learning research by comparing e-exam scores and durations of three separate groups of e-exam takers using different authentication methods: Online Using Username/Password (OLUP), In-Testing Proctored Center (ITPC), and Online Proctored with Multibiometrics (OPMB). The aim was to better understand the role as well as the possible effect of continuous and dynamic multibiometric authentication on professional certification e-exam scores and durations. Our results indicated that group affiliation, i.e. type of authentication methods, had no significant effect on differences among e-exam scores and durations. While there was a clear path of increased mean e-exam score as authentication method was relaxed, it was evident from the analysis that these were not statistically significant,probably due to the limited sample size. Age was found to have a significant effect on e-exam scores where younger participants were found to have higher e-exam scores and lower e-exam durations than older participants. Gender was not found to have a significant effect on e-exam scores nor durations. This study’s results can help organizations better understand the role, possible effect, and potential application of continuous and dynamic multibiometric authentication as a justifiable approach when compared with the more common authentication approach ofUser Identifier (UID) and password, both in professional certification e-exams as well as in an online environment

Towards a model of factors affecting resistance to using multi-method authentication systems in higher-education environments

Over the course of history, different means of object as well as person identification and verification have evolved for user authentication. In recent years, a new concern has emerged regarding the accuracy of authentication and of protection of personal identifying information (PII), because previous information systems (IS) misuses have resulted in significant financial loss. Such losses have escalated more noticeably because of identity-theft incidents due to breaches of PII within multiple public-access environments, such asinstitutions of higher-education. Although the use of various biometric and radio frequency identification (RFID) technologies is expanding, resistance to using these technologies remains an issue. As such, in this research-in-progress paper, we outline a predictive study to assess the contribution of campus students’ perceptions of the importance of protecting their PII, noted as Perceived Value of Organizational Protection of PII (PVOP), authentication complexity (AC), and invasion of privacy (IOP) on their resistance to using multi-method authentication systems (RMS) in higher-education environments. In this work-in-progress study, we seek to better understand the theoretical foundations for the effect of students’ perceptions on their resistance to using multi-method authentication systems (RMS) in higher-education environments and uncover key constructs that may significantly contribute to such resistance. A quasiexperiment is proposed including clearly identified procedures and data analyses

A Survey on Ear Biometrics

Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non contact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though, current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion; earprint forensics; ear symmetry; ear classification; and ear individuality. This paper provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers

An Efficient Secure Multimodal Biometric Fusion Using Palmprint and Face Image

Biometrics based personal identification is regarded as an effective method for automatically recognizing, with a high confidence a person’s identity. A multimodal biometric systems consolidate the evidence presented by multiple biometric sources and typically better recognition performance compare to system based on a single biometric modality. This paper proposes an authentication method for a multimodal biometric system identification using two traits i.e. face and palmprint. The proposed system is designed for application where the training data contains a face and palmprint. Integrating the palmprint and face features increases robustness of the person authentication. The final decision is made by fusion at matching score level architecture in which features vectors are created independently for query measures and are then compared to the enrolment template, which are stored during database preparation. Multimodal biometric system is developed through fusion of face and palmprint recognition