Risk-based neuro-grid architecture for multimodal biometrics

Recent research indicates that multimodal biometrics is the way forward for a highly reliable adoption of biometric identification systems in various applications, such as banks, businesses, government

Multiple Traits for People Identification

Present biometric systems mostly rely on a single physical or behavioral feature for either identification or verification. However, day to day use of single biometries in massive or uncontrolled scenarios still has several shortcomings. These can be due to complex or unstable hardware settings, to changing environmental conditions or even to immature software procedures: some classification problems are intrinsically hard to solve. Possible spoofing of single biometric features is an additional issue. Last but not least, some features may occasionally lack the requisite of universality. As a consequence, biometric systems based on a single feature often have poor reliability, especially in applications where high security is needed. Multimodal systems, i.e., systems that concurrently exploit multiple features, are a possible way to achieve improved effectiveness and reliability. There are several issues that must be addressed when designing such a system, including the choice of the set of biometric features, the normalization method, the integration schema and the fusion process, and the use of a measure of reliability for each subsystem on a single response basis. This chapter describes the state of the art regarding such issues and sketches some suggestions for future work

Multimodal Biometric Authentication Methods: A COTS Approach

We examine the performance of multimodal biometric authentication systems using state-of-the-art Commercial Off-the-Shelf (COTS) fingerprint and face biometrics on a population approaching 1000 individuals. Prior studies of multimodal biometrics have been limited to relatively low accuracy non-COTS systems and populations approximately 10 % of this size. Our work is the first to demonstrate that multimodal fingerprint and face biometric systems can achieve significant accuracy gains over either biometric alone, even when using already highly accurate COTS systems on a relatively large-scale population. In addition to examining well-known multimodal methods, we introduce novel methods of fusion and normalization that improve accuracy still further through population analysis. 1

Performance anchored score normalization for multi-biometric fusion

This work presents a family of novel normalization techniques for score-level multi-biometric fusion. The proposed normalization is not only concerned to bring comparison scores to a common range and scale, it also focuses in bringing certain operational performance points in the distribution into alignment. The Performance Anchored Normalization (PAN) algorithms discussed here were tested on the extended Multi Modal Verification for Teleservices and Security applications database (XM2VTS) and proved to outperform conventional score normalization techniques in most tests. The tests were performed with combination fusion rules and presented as biometric verification performance measures

Second-Level Partition for Estimating FAR Confidence Intervals in Biometric Systems

13th International Conference on Computer Analysis of Images and Patterns, CAIP 2009, Munster, 2-4 September 2009Most biometric authentication algorithms make use of a similarity score that defines how similar two templates are according to a threshold and the accuracy of the results are expressed in terms of a False Reject Rate (FRR) or False Accept Rate (FAR) that is estimated using the training data set. A confidence interval is assigned to any claim of accuracy with 90% being commonly assumed for biometric-based authentication systems. However, these confidence intervals may not be as accurate as is presumed. In this paper, we report the results of experiments measuring the performance of the widely-used subset bootstrap approach to estimating the confidence interval of FAR. We find that the coverage of the FAR confidence intervals estimated by the subset bootstrap approach is reduced by the dependence between two similarities when they come from two individual pairs shared with a common individual. This is because subset bootstrap requires the independence of different subsets. To deal with this, we present a second-level partition to the similarity score set between different individuals, producing what we call a subset false accept rate (SFAR) bootstrap estimation. The experimental results show that the proposed procedures greatly increase the coverage of the FAR confidence intervals.Department of Computin