Training algorithms for robust face recognition using a template-matching approach

This paper describes a complete face recognition system. The system uses a template matching approach along with a training algorithm for tuning the performance of the system to solve two types of problems simultaneously: 1) correct classification experiments which correctly recognize and identify individuals who are in the database; and 2) false positive experiments which reject individuals who are not part of the database. Experimental results are given which indicate that this training method is capable of consistently producing high correct classification rates and low false positive rates