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

39Table 3.4: Example for a heuristic rule. SBS endowed with ρ = 4 categories and a given knownpopulation (distribution in the 4 categories). If our SBS estimates category φ 2 for a subject tobelong into, due to the 0 probability of occurrence, the system decides for the next probablecategory, namely φ 1 .Category µ 1 µ 2 Probability for occurrence of φ i given nφ 1 0 0 0.5φ 2 0 1 0φ 3 1 0 0.3φ 4 1 1 0.2underlying algorithm is used, since it will discriminate some error cases. In the classificationstep we can easily identify subjects classified into "empty" categories. Since those categories areof probability 0 to occur, due to the known population, an intelligent classification system recognizesthese cases and reclassifies those subjects in "similar" categories with higher probabilitiesof occurrence(=non–empty categories). A "similar" category hereby is a category with highestprobability for misclassification, given the wrongly detected category. We here assume that anerror caused by misclassification of one trait is more probable than the misclassification of twoor more traits. For visualization see Table 3.4: if a subject is classified into the category φ 2 , thesystem recognizes thatφ 2 is an empty category and searches for the next probable category, whichin this case would beφ 1 . This simple heuristic rule leads already to a significant reconciliation ofthe added up estimation error of the SBS, see Figure 3.7.0.80.7Interference limited errrorCompensated ErrorError due to Interference and estimation reliability0.60.5Perr0.40.30.20.12 4 6 8 10 12 14 16Subjects NFigure 3.7: Errors in a SBS system: interference limited error, estimation reliability error, compensatederror.In the following we outline an additional error, which can be associated to SBSs when used ina scenario, where the traits are re–identified based on a human description.3.5.4.2 Human machine interactionThe immense benefit of having human understandable and compliant soft biometric traits overclassical biometrics, enables a computer aided biometric search to have as an input a human descriptionof the target subject. The importance of related applications becomes evident in cases,such as a loss of a child in a mall, where the mother can just provide a description of the child andcomputer based search can be performed on available security video material. Along with the benefitof human compliance come though additional quantification and human-machine interactionerrors. Such errors can have different causes.