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

person in the red shirt”. Further significant factors for classifying soft biometric traits are distinctivenessand permanence. Distinctiveness is the strength with which a trait is able to distinguishbetween individuals. As an example ’beard’ has a low distinctiveness, since it can only be appliedto the male part of the population and furthermore possesses only two sub–categories (present ornot). This example points out a certain correlation between distinctiveness and nature of value.Traits with continuous sub-categories are in general more distinctive than traits with discrete andmoreover binary sub-categories. In this context the difference between nature of value and humanlabeling of traits is the following: while hair color has principally different nuances and is thus ofcontinuous character, humans tend to discrete labeling. We adopt this human approach for developedsoft biometric estimation algorithms, detecting for example hair color in categories such asblack, blond, brown, rather than RGB values.The permanence of a trait plays a major role for the application for which a SBS is employed. Asan example an application, where identification within a day is required, will accept low permanencetraits like age, weight or clothing color (inter vs. intra session observation).The final subdivision subjective perception refers to the degree of ambiguity associated in identifyingor labelling specific soft biometric traits sub-categories. We note the relation of subjectiveperception to the nature of value, where an increased amount of subcategories leads to a moredifficult classification. In fact subjectivity lays even in the decision of the nature of value. In otherwords, colors for example can be argued to be continuous, due to the huge variance in nuancesblending into each other, or to be discrete due to the fact that colors can be described by discreteRGB values.We note that soft biometrics can be classified by additional aspects such as accuracy and importance,which are deducible from the named classification classes, depending on the cause forspecification (e.g. suitability for a specific application).Characteristics, advantages and limitationsSoft biometrics has carried in some extent the attributes of classical biometrics over, as thegeneral idea of identification management based on who you are is still being pursuit. The traitsprovide weak biometrical information about the individual and correspondingly have inherited thepredicates to be universal, measurable and acceptable; furthermore the trait’s classification algorithm(s)performance should be able to meet the application’s requirements. To a certain degreealso the aspects uniqueness, permanence and circumvention play a role for soft biometrics, but aretreated to a greater extent flexible.Initially, soft biometric traits have been employed to narrow down the search of a database, in orderto decrease the computational time for the classical biometric trait. An additional applicationis the fusion of soft biometrics and classical biometric traits to increase overall system performance.Soft biometrics impart systems substantial advantages: they can be partly derived frommain detected classical biometric identifier, their acquisition is non intrusive and does not requireenrolment; training can be performed in advance on individuals out of the specific identificationgroup. Summarizing soft biometric traits typically are:– Human compliant: Traits conform with natural human description labels.– Computationally efficient: Sensor and computational requirements are marginal.– Enrolment free: Training of the system is performed off–line and without prior knowledgeof the inspected individuals.– Deducible from classical biometrics: Traits can be partly derived from images captured forprimary (classical) biometric identifier (e.g. eye color from eye images).– Non intrusive: Data acquisition is user friendly or can be fully imperceptible.– Classifiable from a distance: Data acquisition is achievable at long range.– Classifiable pose flexible: Data acquisition is feasible from a number of poses.5