Multimodal Biometrics: Human Recognition Systems - download pdf or read online

By Mark S. Nixon, Tieniu Tan, Rama Chellappa

ISBN-10: 0387244247

ISBN-13: 9780387244242

ISBN-10: 0387264078

ISBN-13: 9780387264073

ISBN-10: 1402076517

ISBN-13: 9781402076510

ISBN-10: 1402078021

ISBN-13: 9781402078026

Trustworthy human authentication schemes are of paramount value in our hugely networked society. Advances in biometrics support handle the myriad of difficulties linked to conventional human reputation tools. The functionality and merits of a biometric method should be considerably stronger through consolidating the proof provided by means of a number of biometric resources. Multibiometric structures are anticipated to satisfy the stringent functionality necessities imposed through large-scale authentication structures.

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Extra info for Multimodal Biometrics: Human Recognition Systems

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Have advocated societal concerns about the use of biometric solutions in large-scale applications. The promotion of Privacy-Enhancing Technologies (PETs) can assuage some of the legitimate concerns associated with biometric-enabled technology (Rejman-Greene, 2005; Kenny and Borking, 2002). For example, the use of personal smart cards to store and process the biometric template of an individual can mitigate public concerns related to placing biometric information in a centralized database. Apart from technological solutions to address privacy concerns, government regulations are also required in order to prevent the inappropriate transmission, exchange and processing of biometric data.

This integration of evidence is known as information fusion and, if appropriately done, can enhance the matching accuracy of a recognition system. Thus, a properly designed multibiometric system can improve matching accuracy, increase population coverage and deter spoofing activities. With biometrics already being chosen to deliver crucial societal functions, it is only a matter of time before multibiometric systems begin to impact the way we perform identity management in the 21st century. Biometrics: When Identity Matters Notes 1 The identity of an individual may be viewed as the information associated with that person in a particular identity management system.

4) p{s\genuine)ds. 6) p{s\genuine)ds. 7 illustrates the genuine and impostor distributions corresponding to a face biometric system. The similarity scores, in this case, are taken from the NIST BSSRl database (see Appendix) and originate from a matcher identified as Face-G. 8(a)). When a linear, logarithmic or semi-logarithmic scale is used to plot these error rates, then the resulting graph is known as a Receiver Operating Characteristic (ROC) curve (Egan, 1975). 7. The genuine and impostor distributions corresponding to tlie Face-G matcher in the NIST BSSRl database.

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Multimodal Biometrics: Human Recognition Systems by Mark S. Nixon, Tieniu Tan, Rama Chellappa

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