Empirical capacity of a biometric channel under the constraint of global PCA and ICA encoding

The ability of practical biometric systems to recognize a large number of subjects is constrained by a variety of factors that include a choice of a source encoding technique, quality of images, complexity and variability of underlying patterns and of collected data. Given a source encoding technique, the remaining factors can be attributed to distortions due to a biometric recognition channel. In this work, we define empirical mutual information and recognition rate and evaluate empirical recognition capacity of biometric systems under the constraint of two global encoding techniques: principal component analysis (PCA) and independent component analysis (ICA). The empirical capacity of biometric systems is numerically evaluated as a point of intersection of the empirical mutual information rate curve plotted as a function of the recognition rate and the diagonal line bisecting the first quadrant. The developed methodology is applied to find the empirical capacity of different recognition channels formed during acquisition of different iris and face databases.