Book Image

Identity Management with Biometrics

By : Lisa Bock
Book Image

Identity Management with Biometrics

By: Lisa Bock

Overview of this book

Biometric technologies provide a variety of robust and convenient methods to securely identify and authenticate an individual. Unlike a password or smart card, biometrics can identify an attribute that is not only unique to an individual, but also eliminates any possibility of duplication. Identity Management with Biometrics is a solid introduction for anyone who wants to explore biometric techniques, such as fingerprint, iris, voice, palm print, and facial recognition. Starting with an overview of biometrics, you’ll learn the various uses and applications of biometrics in fintech, buildings, border control, and many other fields. You’ll understand the characteristics of an optimal biometric system and then review different types of errors and discover the benefits of multi-factor authentication. You’ll also get to grips with analyzing a biometric system for usability and accuracy and understand the process of implementation, testing, and deployment, along with addressing privacy concerns. The book outlines the importance of protecting biometric data by using encryption and shows you which factors to consider and how to analyze them before investing in biometric technologies. By the end of this book, you’ll be well-versed with a variety of recognition processes and be able to make the right decisions when implementing biometric technologies.
Table of Contents (20 chapters)
1
Section 1 –Understanding Biometric Authentication
6
Section 2 – Applying Biometric Technologies
12
Section 3 – Deploying a Large-Scale Biometric System

Summary

We have taken a look at how using a voice as an identifier is a behavioral biometric, in that it represents the manner in which a subject speaks. We then reviewed the many things that can influence the way we speak, such as medical conditions and aging. We examined the advances of VRT over the years and its similarity to speech recognition. We saw how to use statistical methods such as HMM, which helps recognize speech by looking for predictive patterns to change the state.

We reviewed how a voice is digitized and transmitted and learned how performance can be impacted by transmission errors and inferior equipment. We then stepped through the process of what happens from enrollment to matching. We compared text-dependent versus text-independent methods, and then compared matching methods that use either template matching or feature analysis. Finally, we saw how using VRT to quickly provide identification and authentication can streamline the overall experience and enhance...