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

5AbstractThis dissertation studies soft biometrics traits, their applicability in different security and commercialscenarios, as well as related usability aspects. We place the emphasis on human facial softbiometric traits which constitute the set of physical, adhered or behavioral human characteristicsthat can partially differentiate, classify and identify humans. Such traits, which include characteristicslike age, gender, skin and eye color, the presence of glasses, moustache or beard, inheritseveral advantages such as ease of acquisition, as well as a natural compatibility with how humansperceive their surroundings. Specifically, soft biometric traits are compatible with the humanprocess of classifying and recalling our environment, a process which involves constructions ofhierarchical structures of different refined traits.This thesis explores these traits, and their application in soft biometric systems (SBSs), andspecifically focuses on how such systems can achieve different goals including database searchpruning, human identification, human re–identification and, on a different note, prediction andquantification of facial aesthetics. Our motivation originates from the emerging importance ofsuch applications in our evolving society, as well as from the practicality of such systems. SBSsgenerally benefit from the non-intrusive nature of acquiring soft biometric traits, and enjoy computationalefficiency which in turn allows for fast, enrolment–free and pose–flexible biometricanalysis, even in the absence of consent and cooperation by the involved human subjects. Thesebenefits render soft biometrics indispensable in applications that involve processing of real lifeimages and videos.In terms of security, we focus on three novel functionalities of SBSs: pruning the search inlarge human databases, human identification, and human re–identification.With respect to human identification we shed some light on the statistical properties of pertinentparameters related to SBSs, such as employed traits and trait–instances, total categories,size of authentication groups, spread of effective categories and correlation between traits. Furtherwe introduce and elaborate on the event of interference, i.e., the event where a subject picked foridentification is indistinguishable from another subject in the same authentication group.Focusing on search pruning, we study the use of soft biometric traits in pre-filtering largehuman image databases, i.e., in pruning a search using soft biometric traits. Motivated by practicalscenarios such as time–constrained human identification in biometric-based video surveillancesystems, we analyze the stochastic behavior of search pruning, over large and unstructured datasets which are furthermore random and varying, and where in addition, pruning itself is not fullyreliable but is instead prone to errors. In this stochastic setting we explore the natural tradeoff thatappears between pruning gain and reliability, and proceed to first provide average–case analysisof the problem and then to study the atypical gain-reliability behavior, giving insight on how oftenpruning might fail to substantially reduce the search space. Moreover we consider actual softbiometric systems (nine of them) and the corresponding categorization algorithms, and provide anumber of experiments that reveal the behavior of such systems. Together, analysis and experimentalresults, offer a way to quantify, differentiate and compare the presented SBSs and offerinsights on design aspects for improvement of such systems.With respect to human re–identification we address the problem of pose variability in surveillancevideos. Despite recent advances, face-recognition algorithms are still challenged when appliedto the setting of video surveillance systems which inherently introduce variations in the poseof subjects. We seek to provide a recognition algorithm that is specifically suited to a frontal-tosidere-identification setting. Deviating from classical biometric approaches, the proposed methodconsiders color- and texture- based soft biometric traits, specifically those taken from patches of