Discriminant Analysis Diagram for Pattern Recognition

Abstract

In this lecture notes a novel point of view onto discriminant models used for biometric verification is presented. Various linear discriminant algorithms based on Fisher-like class separation measures are incorporated into discriminant analysis diagram (DAD). This new methodology can be used for design of special class of pattern recognition systems. Namely, pattern recognition embracing verification, identification, and indexing of patterns are based on intra-class errors when pattern classes used in training time are different than classes recognized in system exploiting time. This is typical case in biometric identity verification. The point is illustrated well by analysis of recent advances in development efface recognition algorithms.