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

14 1. INTRODUCTIONspecific scenario of applying soft biometrics for human frontal-to-side re-identification. We thenchange gear and deviate from security related applications to the more commercially oriented applicationof employing soft biometrics in quantifying and predicting female facial aesthetics. Theabove approaches are then complemented by a more practical automatic soft biometric classificationtool that we present. Finally, motivated by human acceptance issues, we proceed to provide ausability study relating to soft biometrics.1.1 Achievements and structure of the dissertationWe proceed with an explicit description of the structure of the thesis, and the introduction ofthe scenarios / applications of interest in each chapter.Chapter 2 - Soft biometrics: characteristics, advantages and related workIn Chapter 2 we offer general considerations related to soft biometrics. Firstly in Section 2.1we introduce a candidate list of traits and furthermore proceed to portray pertinent advantages andlimitations in Section 2.2. We then identify in Section 2.3 previous work on soft biometric traits.Chapter 3 - Bag of facial soft biometrics for human identificationChapter 3 considers the case where a SBS can distinguish between a set of traits (categories),which set is large enough to allow for the classification that achieves human identification. Theconcept of person identification based on soft biometrics originates in the way humans performface recognition. Specifically human minds decompose and hierarchically structure complex problemsinto fractions and those fractions into further sub-fractions, see [Ley96], [Sim96]. Consequentlyface recognition performed by humans is the division of the face in parts, and subsequentclassification of those parts into categories. Those categories can be naturally of physical, adheredor behavioral nature and their palette includes colors, shapes or measurements, what we refer tohere as soft biometrics. The key is that each individual can be categorized in terms of such characteristics,by both humans or by image processing algorithms. Although features such as hair,eye and skin color, facial hair and shape, or body height and weight, gait, cloth color and humanmetrology are generally non distinctive, a cumulative combination of such features providesan increasingly refined and explicit description of a human. SBSs for person identification haveseveral advantages over classical biometric systems, as of non intrusiveness, computational andtime efficiency, human compliance, flexibility in pose- and expression-variance and furthermorean enrolment free acquirement in the absence of consent and cooperation of the observed person.Soft biometrics allow for a reduced complexity determination of an identity. At the same timethough, the named reduced computational complexity comes with restrictions on the size of anauthentication group. It becomes apparent that a measure of performance must go beyond theclassical biometric equal error rate of the employed detectors and include a different and newparametrization. Our general interest here is to provide insightful mathematical analysis of reliabilityof general soft biometric systems, as well as to concisely describe the asymptotic behavior ofpertinent statistical parameters that are identified to directly affect performance. Albeit its asymptoticand mathematical nature, the approach aims to provide simple expressions that can yieldinsight into handling real life surveillance systems.In Chapter 3, Section 3.1 introduces the operational setting of a SBS. In this setting we elaborateon pertinent factors, such as those of the authentication group, traits, traits instances, overallcategories and their interrelations. We then proceed in Section 3.5.1 to introduce and explain the