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

60 4. SEARCH PRUNING IN VIDEO SURVEILLANCE SYSTEMS4.8 SummaryThe current chapter provided statistical analysis of the gain and reliability in pruning the searchover large data sets, where these sets are random and where there is a possibility that the pruningmay entail errors. In this setting, pruning plays the role of pre-filtering, similar to techniques suchas video indexing. The analysis may offer insight on better designing pre-filtering algorithms fordifferent search settings. We further studied nine different, actual, soft biometric systems, as wellas analyzed and experimented with factors like average error, pruning gain and goodput. Usingthese factors, we provided a quantifiable comparison of these systems. Furthermore we identifiedrelations between SBS enhancement, error probabilityP err , pruning gain r and goodput U. Thesefindings bring to the fore some SBS design aspects. Finally we gave insight on the computationalcost reduction related to person recognition systems with a pruning mechanism. This insight revealedsome of the benefits of applying SBS for pre filtering.We here studied and analyzed the pertinent characteristics related to search pruning performedby SBSs. In the next chapter we examine a third scenario (after human identification and pruningthe search), namely human re-identification. In such a scenario in what follows, we explore thecapability and limitations of existing SB algorithms. We hereby introduce an additional challengeof frontal-to-side pose variation.