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

58 4. SEARCH PRUNING IN VIDEO SURVEILLANCE SYSTEMSSBS P err r U‘2e’ 0.0750 0.4743 0.4388‘m’ 0.1420 0.2538 0.2177‘g’ 0.0690 0.3275 0.3049‘4e’ 0.1522 0.7039 0.5968‘mg’ 0.2012 0.4982 0.3979‘2em’ 0.2063 0.6077 0.4823‘2eg’ 0.1388 0.6465 0.5568‘2emg’ 0.2611 0.7362 0.5440‘4emg’ 0.3227 0.8514 0.5766Table 4.2: Pruning error, gain and goodput of the proposed SBSsError − Gain − Goodput0.90.80.70.60.50.40.30.2Prob of ErrorAverage GainGoodput0.102e m g 4e mg 2em 2eg 2emg 4emgSB SystemFigure 4.11: Pruning error, gain and goodput of the proposed SBSs.the gain or the error contribution depending on the application scenario. If we are interested inpruning aggressively a big database, we would emphasize on the gain and choose a system like‘4emg’, at the cost though of an increased FRR. If we want to make a conservative search prunewith a low risk of pruning out the target subject, then system ‘2eg’ is more suitable to be oursystem of choice. The question arises here of how to bound the case when an enhancement of asystem is profitable in terms of goodput. With other words is an addition of a soft biometric traita positive contribution to the SBS or does the related average error outweighs the related pruninggain? This is addressed in Equation (4.30) and can be answered depending on a given application,population statistics and error probability.4.7.2 Computational cost reductionWe briefly discuss the computational savings that result by preceding a computationally expensivealgorithm (e.g. full person recognition) with pruning based on generally simpler categorizationalgorithms. In terms of analysis, letT be the total number of soft biometric traits (e.g. system‘4emg’ corresponds to T = 3 traits: t = 1 for eyes, t = 2 for moustache, t = 3 for glasses). Furthermorelet N t be the average computational complexity required to implement categorization,