Smart Cards to Enhance Security and Privacy in Biometrics

Smart cards are portable secure devices designed to hold personal and service information for many kind of applications. Examples of the use of smart cards are cell phone user identification (e.g. GSM SIM card), banking cards (e.g. EMV credit/debit cards) or citizen cards. Smart cards and Biometrics can be used jointly in different kinds of scenarios. Being a secure portable device, smart cards can be used for storing securely biometric references (e.g. templates) of the cardholder, perform biometric operations such as the comparison of an external biometric sample with the on-card stored biometric reference, or even relate operations within the card to the correct execution and result of those biometric operations. In order to provide the reader of the book with an overview of this technology, this chapter provides a description of smart cards, from their origin till the current technology involved, focusing especially in the security services they provide. Once the technology and the security services are introduced, the chapter will detail how smart cards can be integrated in biometric systems, which will be summarized in four different strategies: Store-on-Card, On-Card Biometric Comparison, Work-sharing Mechanism, and System-on-Card. Also the way to evaluate the joint use of smart cards and Biometrics will be described; both at the performance level, as well as its security. Last, but not least, this chapter will illustrate the collaboration of both technologies by providing two examples of current major deployments.Publicad

Signal specific electric potential sensors for operation in noisy environments

Limitations on the performance of electric potential sensors are due to saturation caused by environmental electromagnetic noise. The work described involves tailoring the response of the sensors to reject the main components of the noise, thereby enhancing both the effective dynamic range and signal to noise. We show that by using real-time analogue signal processing it is possible to detect a human heartbeat at a distance of 40 cm from the front of a subject in an unshielded laboratory. This result has significant implications both for security sensing and biometric measurements in addition to the more obvious safety related applications

Using biometrics authentication via fingerprint recognition in e-Exams in e-Learning environment

E-learning is a great opportunity for modern life. Notably, however, the tool needs to be coupled with efficient and reliable security mechanisms to ensure the medium can be established as a dependable one. Authentication of e-exam takers is of prime importance so that exams are given by fair means. A new approach shall be proposed so as to ensure that no unauthorised individuals are permitted to give the exams

Biometric iris templates security based on secret image sharing and chaotic maps

Biometric technique includes of uniquely identifying person based on their physical or behavioural characteristics. It is mainly used for authentication. Storing the template in the database is not a safe approach, because it can be stolen or be tampered with. Due to its importance the template needs to be protected. To treat this safety issue, the suggested system employed a method for securely storing the iris template in the database which is a merging approach for secret image sharing and hiding to enhance security and protect the privacy by decomposing the template into two independent host (public) iris images. The original template can be reconstructed only when both host images are available. Either host image does not expose the identity of the original biometric image. The security and privacy in biometrics-based authentication system is augmented by storing the data in the form of shadows at separated places instead of whole data at one. The proposed biometric recognition system includes iris segmentation algorithms, feature extraction algorithms, a (2, 2) secret sharing and hiding. The experimental results are conducted on standard colour UBIRIS v1 data set. The results indicate that the biometric template protection methods are capable of offering a solution for vulnerability that threatens the biometric template

Binary Biometrics: An Analytic Framework to Estimate the Bit Error Probability under Gaussian Assumption

In recent years the protection of biometric data has gained increased interest from the scientific community. Methods such as the helper data system, fuzzy extractors, fuzzy vault and cancellable biometrics have been proposed for protecting biometric data. Most of these methods use cryptographic primitives and require a binary representation from the real-valued biometric data. Hence, the similarity of biometric samples is measured in terms of the Hamming distance between the binary vector obtained at the enrolment and verification phase. The number of errors depends on the expected error probability Pe of each bit between two biometric samples of the same subject. In this paper we introduce a framework for analytically estimating Pe under the assumption that the within-and between-class distribution can be modeled by a Gaussian distribution. We present the analytic expression of Pe as a function of the number of samples used at the enrolment (Ne) and verification (Nv) phases. The analytic expressions are validated using the FRGC v2 and FVC2000 biometric databases