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

24 2. SOFT BIOMETRICS: CHARACTERISTICS, ADVANTAGES AND RELATED WORKfrom videos and exploited in a multiple camera video surveillance scenario in order to track thesubjects that cross the surveillance network. In [LLZ06] an approach for recognizing the gender,ethnicity and age with facial images is proposed. The approach incorporates Gabor filter, Adaboostlearning as well as support vector machine classifiers. A further hybrid classification basedon gender and ethnicity is considered in [GPW98] and [GW99]. The hybrid approach consistsof an ensemble of radial basis function networks and inductive decision trees. The authors showrobustness and good performance. A different approach for analysis in hybrid soft biometric systemsis provided in [SGN08] and [RN10], where semantic information (which corresponds to softbiometric classifiers) is manually extracted from a series of videos. Using the analysis of variancethe authors select a pool of traits which are considered the most representative. Those traits arethen used together with gait information. The authors demonstrate that the additional informationprovided by the semantic traits increases the performance of the people recognition system basedon gait. Those results are extended in [RNS11] and in [RN11]. The authors in [ACPR10] go onestep further and study the relation of human body measures, which allows for certain applicationsthe prediction of missing body measures. In [VFT + 09] the authors propose an approach for peoplesearch in surveillance data, characterized by three main elements: sensors, body parts, and theirattributes. The body parts and attributes are here closely related to soft biometrics.2.4 Domains of applicationSoft biometrics are either employed as uni modal systems, classifying a single trait classifiers,or in a combination with other systems. We differentiate following main domains of application.Fusion with classical biometric traits : SBSs are incorporated in multi modal biometricalsystems with the goal of increasing the overall reliability. Such an approach has been followed, in[JDN04b], where the benefits of soft biometrics in addition to fingerprint lead to an improvementof approximately 5% over the primary biometric system.Pruning the search : SBS were employed in previous works to pre filter large biometricdatabases with the aim of higher search efficiency. Scientific work on using soft biometricsfor pruning the search can be found in [KBN08, KBBN09], where a multitude of attributes,like age, gender, hair and skin color were used for classification of a face database, as well asin [GBDB97, New95] where the impact of pruning traits like age, gender and race was identifiedin enhancing the performance of regular biometric systems.A third application is the employment of a multi modal SBS with the goal of human identificationor human re-identification.Human (re-)identification : For human (re-)identification the soft biometric trait related limitationsof distinctiveness and permanence are overcome by combining multiple traits. The concept ofBag of Soft Biometrics(BoSB) is directly inspired from the idea of Bag of Words [Joa98, WPS06]and Bag of Features [LSP06] developed under the context of text mining and content based imageretrieval. For the BoSB the “items” of the bag are soft biometric signatures extracted from thevisual appearance of the subject.Other possible applications relate to the ability to match people based on their biometric-traitpreferences, acquiring statistical properties of biometric identifiers of groups, avatar modellingbased on the instantaneous facial characteristics (glasses, beard or different hair color), statisticalsampling of audiences, and many others.