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

21is 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.– Not requiring the individual’s cooperation: Consent and contribution from the subject aregenerally not needed.– Preserving human privacy: The stored signatures are visually available to everyone andserve in this sense privacy.The plethora or utilities has motivated an increasing number of research activities related tosoft biometrics. In the next section we give an overview of scientific work gaining from the benefitsrelated to soft biometrics.2.3 Related workIn this section we outline work, pertinent to soft biometrics. This overview does not claim tobe an exhaustive state of the art, but rather a highlight selection on performed scientific studies.Soft biometrics is a relatively novel topic and related work enfolds over several research fields.Recent work can be mainly classified in three research fields:1. The first and largest field includes the study and identification of traits and associated imageprocessing algorithms for classification and detection of such.2. The second fast growing field identifies operational scenarios for the aforementioned algorithmsand provides experimental results for such scenarios.3. The third and smallest field comprises of the global and theoretical investigation of theemployment of soft biometrics applications and related studies.Scientific works belonging to the first field cover algorithms for traits such as iris pattern, seein [SBS10], or facial marks, see in [JP09]. A broader overview of work from the first group isreferenced in the following sections 2.3.1 and 2.3.2.The second field can be sub-classified in subgroups which differentiate the way soft biometricsare employed, as stand–alone systems, as pre-filtering mechanisms of bigger systems, or as fusedparallel systems. Related scenarios include continuous authentication [NPJ10], video surveillancesee [DFBS09], [FDL + 10], [MKS10], person verification [ZESH04] and moreover person identification[PJ10]. An interesting recent associated scenario for SBS based person identification isthe recognition of faces in triage images of mass disaster situations [CO11]. Further examples aregiven in section 2.3.3.Finally the third field involves studies on the placement of soft biometrics in applications suchas forensics [JKP11] and human metrology [ACPR10].