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

101ConclusionsThis dissertation explored the role of soft biometrics in security related applications, as wellas in quantifying and predicting female facial aesthetics. Our analysis was accompanied by constructionsof practical trait classification algorithms that were tested on existing image databases.We also performed a usability study of systems that employ such soft biometric identifiers.In terms of security, we focused on three related applications, namely: a) applying SBSs toachieve complete person identification, b) applying SBSs to prune a large database in order toreduce the search space, and c) applying soft biometrics for person re-identification, with a focuson the frontal vs. side scenario.Applying SBSs to achieve complete person identificationWe explored in this context the use of multi-trait SBSs for human identification, studyinganalytically the identification capabilities of the system, as a function of the authentication groupv, its size n, the featured categories ρ, and the effective categories F . We showed that in theinterference limited setting, for a given randomly chosen authentication group v, of a given sizen, then the reliability of identification (averaged over the subjects in v) is a function only of thenumber of non-empty categories F(v). Then we provided statistical analysis of this reliability,over large populations. The latter part provided bounds that, in the interference limited settingsuggest an exponential reduction in the probability of interference patterns, as a result of a linearincrease inρ.Applying SBSs to prune a large database in order to reduce the search spaceWe provided statistical analysis of the gain and reliability in pruning the search over largedata sets, where these sets are random and where there is a possibility that the pruning may entailerrors. In this setting, pruning plays the role of pre-filtering, similar to techniques such asvideo indexing. The average-case analysis presented here, described the typical assistance thatpruning provides in reducing the search space, whereas large-deviations based analysis providedinsight as to how often pruning can behave in an atypically unhelpful, or atypically helpful manner.The analysis may offer insight on better designing pre-filtering algorithms for different searchsettings. We further studied nine different, actual, soft biometric systems, as well as analyzed andexperimented with factors like average error, pruning gain and goodput. Using these factors, weprovided a quantifiable comparison of these systems. Furthermore we identified relations betweenSBS enhancement, error probabilityP err , pruning gain r and goodput U. These findings bring tothe fore some SBS design aspects. We also gave insight on the computational cost reduction thatcan be introduced by SBS-based pruning in the setting of person recognition.