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

HIT Conformance Testing: Advancing Syndromic Surveillance System Interoperability

This session describes the Syndromic Surveillance Messaging Validation Suite being developed collaboratively by experts from NIST, ISDS, and CDC. The session explains the features and functions of the validation suite; describes how it will be used for ONC HIT certification testing in 2016; provides details about the test scenarios (including test stories, test data, and example conformant messages); and explains how using the validation suite for local implementation can enhance standard conformance and improve the quality of SyS data provided to public health agencies by hospitals, emergency departments, and urgent care centers

NIST Syndromic Surveillance Test Suite 2015 Edition

ObjectiveThe NIST Syndromic Surveillance Test Suite for 2015 EditionONC certification testing was published in February 2016. Keyinformation related to the purpose, development, and use of thisconformance test tool is provided via snapshots on a poster.IntroductionDetails about the ONC 2015 Edition certification criteria forSyndromic Surveillance and the related NIST Test Suite wereexplained previously. We now provide an overview and keyinformation regarding updates to the Test Suite and how it is designedto be used.MethodsSnapshots are provided on a poster and are used by the presenterto explain the steps involved in developing the NIST SyndromicSurveillance Test Suite 2015 Edition, to show key features of andupdates to the Test Suite, and to illustrate the relationship of the TestSuite to various releases of the PHIN Messaging Guide for SyndromicSurveillance.ResultsThe NIST Syndromic Surveillance Test Suite for 2015 EditionONC certification testing was published in February 2016. As thetarget stakeholders began using it and providing feedback, this tooland associated documentation were updated. The Test Suite is beingused by test labs for ONC certification testing of health informationtechnologies, by developers in preparation for certification testing,and ultimately by public health jurisdictions for on-boarding ofprovider organizations that need to submit surveillance data.ConclusionsAutomated conformance test tools enable validation of healthinformation technologies’ ability to support the requirementspublished in the PHIN Messaging Guide for Syndromic Surveillance.Having this standard and the means to validate conformance helpsdrive the industry toward the level of interoperability needed topromote efficient reporting and utilization of syndrome-based publichealth surveillance information