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

10Figure 2: System overview.tion and subsequent elimination of soft biometric-based categories, within the context of a searchwithin large databases (Figure 2). In the context of this work, the elimination or filtering our ofthe employed categories is based on the soft biometric characteristics of the subjects. The pruneddatabase can be subsequently processed by humans or by a biometric such as face recognition.Towards analyzing the pruning behavior of such SBSs, we introduce the concept of pruninggain which describes, as a function of pruning reliability, the multiplicative reduction of the setsize after pruning. For example a pruning gain of 2 implies that pruning managed to halve thesize of the original set. We provide average case analysis of the pruning gain, as a function ofreliability, but also moreover an atypical-case analysis, offering insight on how often pruningfails to be sufficiently helpful. In the process we provide some intuition through examples ontopics such as, how the system gain-reliability performance suffers with increasing confusabilityof categories, or on whether searching for a rare looking subject renders the search performancemore sensitive to increases in confusability, than searching for common looking subjects.We then take a more practical approach and present nine different soft biometric systems, anddescribe how the employed categorization algorithms (eye color detector, glasses and moustachedetector) are applied on a characteristic database of 646 people. We furthermore provide simulationsthat reveal the variability and range of the pruning benefits offered by different SBSs. Wederive concise closed form expressions on the measures of pruning gain and goodput, providesimulations, as well as derive and simulate aspects relating to the complexity costs of differentsoft biometric systems of interest.Frontal-to-side person re-identificationTypically biometric face-recognition algorithms are developed, trained, tested and improvedunder the simplifying assumption of frontal-to-frontal person recognition. Such algorithms thoughare challenged when facing scenarios that deviate from the training setting, such as for examplein the presence of non-constant viewpoints, including the frontal-to-side scenario. Most personrecognition algorithms, whether holistic or based on facial features, only manage to optimally handlepose differences that are less than about15 degrees. As a result, a variation in the pose is oftena more dominant factor than a variation of subjects. This aspect of pose variation comes to the forein video surveillance, where a suspect may be pictured firstly frontal, whereas the correspondingtest images could be captured from the side, thus introducing a frontal-to-side recognition problem.Towards handling this problem, we employ multiple soft biometrics related traits. One of ourtasks here is to get some insight into the significance of these traits, specifically the significanceof using hair, skin and clothes patches for frontal-to-side re-identification. We are working on