Guide to Biometrics
Springer Science & Business Media, Jun 29, 2013 - Computers - 364 pages
Starting with fingerprints more than a hundred years ago, there has been ongoing research in biometrics. Within the last forty years face and speaker recognition have emerged as research topics. However, as recently as a decade ago, biometrics itself did not exist as an independent field. Each of the biometric-related topics grew out of different disciplines. For example, the study of fingerprints came from forensics and pattern recognition, speaker recognition evolved from signal processing, the beginnings of face recognition were in computer vision, and privacy concerns arose from the public policy arena. One of the challenges of any new field is to state what the core ideas are that define the field in order to provide a research agenda for the field and identify key research problems. Biometrics has been grappling with this challenge since the late 1990s. With the matu ration of biometrics, the separate biometrics areas are coalescing into the new discipline of biometrics. The establishment of biometrics as a recognized field of inquiry allows the research community to identify problems that are common to biometrics in general. It is this identification of common problems that will define biometrics as a field and allow for broad advancement.
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Authentication and Biometrics
Basic System Errors
Identification System Errors
Selecting a Biometric
Creating and Maintaining Databases
Other editions - View all
access control accuracy algorithms attack authentication protocol biometric application biometric authentication system biometric data biometric database biometric identifiers biometric matcher biometric samples biometric search biometric system biometric template bootstrap candidate list Chapter computed confidence interval cost credentials decision defined detected determined different biometrics distribution eigenface enrollment policy error rates estimate example exception handling face images face recognition False Accept Rate False Match False Negative False Positive False Reject Rate finger forgery FRVT function Hamming distance hand geometry Hence identification system identity individual input integrity iris iriscode match engine match scores measure medium metric minutiae mismatch scores NIST non-match scores operating point password percent performance person population Prob problem query recognition systems replay attacks ROC curve search engine Section sensor shown in Figure smartcard speaker recognition specific statistics Table threshold trade-off vector