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

44 4. SEARCH PRUNING IN VIDEO SURVEILLANCE SYSTEMSfurther elimination of categories, which limits large databases of subjects to a fraction of the initialdatabase, see Figure 4.1. In the context of this chapter the elimination or filtering of the employedcategories is based on the soft biometric characteristics of the subjects. The pruned database canbe subsequently processed by humans or by a biometric such as face recognition.The approach of pruning the search using SBSs, can apply to several re-identification scenarios,including the following:– A theft in a crowded mall is observed by different people who give partial information aboutthe thief’s appearance. Based on this information, a first-pass search applies SBS methodsto cut down on the long surveillance video recordings from several cameras.– A mother has lost her child and can describe traits like clothes color and height of the child.Video surveillance material can be pruned and resulting suggestions can be displayed to themother.The above cases support the applicability of SBSs, but also reveal that together with the benefitsof such systems, come considerable risks such as that of erroneously pruning out the target of thesearch. This brings to the fore the need to jointly analyze the gains and risks of such systems.In the setting of human identification, we consider the scenario where we search for a specificsubject of interest, denoted as v ′ , belonging to a large and randomly drawn authentication groupv of n subjects, where each subject belongs to one of ρ categories. The elements of the set(authentication group) v are derived randomly from a larger population, which adheres to a setof population statistics. A category corresponds to subjects who adhere to a specific combinationof soft biometric characteristics, so for example one may consider a category consisting of blond,tall, females. We note the analogy to the scenario from chapter 3, but proceed to elaborate on thedifferent goal of the current chapter.With n being potentially large, we seek to simplify the search for subject v ′ within v by algorithmicpruning based on categorization, i.e., by first identifying the subjects that potentiallybelong to the same category as v ′ , and by then pruning out all other subjects that have not beenestimated to share the same traits as v ′ . Pruning is then expected to be followed by careful searchof the remaining unpruned set. Such categorization-based pruning allows for a search speedupthrough a reduction in the search space, from v to some smaller and easier to handle set S whichis the subset of v that remains after pruning, see Figure 4.1 and Figure 4.4. This reduction thoughhappens in the presence of a set of categorization error probabilities {ɛ f }, called confusion probabilities,that essentially describe how easy it is for categories to be confused, hence also describingthe probability that the estimation algorithm erroneously prunes out the subject of interest, byfalsely categorizing it. This confusion set, together with the set of population statistics {p f } ρ f=1which describes how common a certain category is inside the large population, jointly define thestatistical performance of the search pruning, which we will explore. The above aspects will beprecisely described later on.Example 7 An example of a sufficiently large population includes the inhabitants of a certaincity, and an example of a randomly chosen authentication group (n-tuple) v includes the set ofpeople captured by a video surveillance system in the aforementioned city between 11:00 and11:05 yesterday. An example SBS could be able to classify 5 instances of hair color, 6 instances ofheight and 2 of gender, thus being able to differentiate betweenρ = 5·6·2 = 60 distinct categories.An example search could seek for a subject that was described to belong to the first category of,say, blond and tall females. The subject and the rest of the authentication group of n = 1000people, were captured by a video-surveillance system at approximately the same time and placesomewhere in the city. In this city, each SBS-based category appears with probability p 1 ,··· ,p 60 ,and each such category can be confused for the first category with probability ɛ 2 ,··· ,ɛ 60 . The