Framework for Analyzing Biometric Template Aging and Renewal Prediction

摘要

Biometric technology and systems are modernizing identity capabilities. With maturing biometrics in full, rapid development, a higher accuracy of identity verification is required. An improvement to the security of biometric-based verification systems is provided through higher accuracy; ultimately reducing fraud, theft, and loss of resources from unauthorized personnel. With trivial biometric systems, a higher acceptance threshold to obtain higher accuracy rates increase false rejection rates and user unacceptability. However, maintaining the higher accuracy rate enhances the security of the system. An area of biometrics with a paucity of research is template aging and renewal prediction, specifically in regards to facial aging. Through the methods presented in this research, higher accuracy rates are obtained without lowering the acceptance threshold, therefore improving the security level, false rejection rates, and user acceptability. As a proof of concept, this research develops a biometric template aging and renewal prediction framework currently absent in the biometric literature. The innovative framework is called the Carls Template Aging and Renewal Prediction Framework (CTARP Framework). The research integrates a diversity of disparate developments to provide a critical fundamental framework of significant advancement in the biometrics body of knowledge. This research presents the CTARP Framework, a novel foundational framework for methods of modeling and predicting template aging and renewal prediction based on matching score analysis. The groundwork discusses new techniques used in the template aging and renewal prediction framework, to include 'perfect match score matrix', 'error score matrix', and 'decay error estimate' concepts.