FACIAL SOFT BIOMETRICS - Library of Ph.D. Theses | EURASIP

54 4. SEARCH PRUNING IN VIDEO SURVEILLANCE SYSTEMSWe now proceed with the application of the derived measures on real life SBSs and furthermorequantify these SBSs.4.6 Practical application of above analysis on proposed SBSsWe adopt the results of 3 real soft biometric trait categorization algorithms from chapter 7 byspecifically taking over the related confusion matrices. These algorithms are error prone systemsfor– categorization of 4 eye colors: based on Gaussian Mixture Models with expectation maximizationclassification of hue and saturation values in the iris,– moustache detection: based on skin color and hair color comparison in the region below thenose,– glasses detection: based on edge and line detection between the eyes.Using the three traits, we construct nine different SBSs which we list below in Table 4.1. All relatedconfusion matrices and related population statistics are listed in Appendix B. The large populationin which we employ those SBSs is based on the statistics of the FERET database [Fer11].For this purpose we annotated the 646 subjects in the FERET database in terms of glasses, moustacheand eye color.SBS Description ρ‘2e’ Categorization of 2 eye colors 2‘m’ Moustache detection 2‘g’ Glasses detection 2‘4e’ Categorization of 4 eye colors 4‘mg’ Moustache and Glasses detection 4‘2em’ 2 eye color categories and moustache detection 4‘2eg’ 2 eye color categories and glasses detection 4‘2emg’ 2 eye color classes, moustache and glasses detection 8‘4emg’ 4 eye color classes, moustache and glasses detection 16Table 4.1: SBSs labeling and description of the ρ associated categoriesIn the following we analyze the pruning gain related to the presented systems.4.6.1 Simulations: the instantaneous pruning gainA pertinent characteristic of an SBS is the amount by which the initial database is reduced. Asa measure of this we adopt the pruning gain from above to be:r(v) := 1− |S|n∈ [0,1], (4.28)describing the fraction of subjects from v which was pruned out. This ranges from 0 (no pruninggain) to 1.We proceed to illustrate the variability ofr(v), as a function ofvbut also ofv ′ . To understandthis variability we can note that ifv ′ belongs in a rare category (e.g. green eyes), then we generallyexpect a higher gain, than if v ′ belonged in a more common category (e.g. black eyes). Similarly,if v happens to comprise of people who look similar to v ′ then the gain will be smaller than thecase where another v comprised of people who looked sufficiently different from v ′ .To illustrate these relationships we proceed with some clarifying simulations involving thepresented SBSs. In these simulations we randomly pick 100 realizations of v, each consisting of