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

16 1. INTRODUCTIONsystems, and describe how the employed categorization algorithms (eye color detector, glasses andmoustache detector) are applied on a characteristic database of 646 people. In the same Section wefurthermore provide simulations that reveal the variability and range of the pruning benefits offeredby different SBSs. In Section 4.7 we derive concise closed form expressions on the measures ofpruning gain and goodput, provide simulations, as well as derive and simulate aspects relating tothe complexity costs of different soft biometric systems of interest.Chapter 5 - Frontal-to-side person re-identificationTypically biometric face-recognition algorithms are developed, trained, tested and improvedunder the simplifying assumption of frontal-to-frontal person recognition. Such algorithms thoughare challenged when facing scenarios that deviate from the training setting, such as for examplein the presence of non-constant viewpoints, including the frontal-to-side scenario. Most personrecognition algorithms, whether holistic or based on facial features, only manage to optimally handlepose differences that are less than about15 degrees. As a result, a variation in the pose is oftena more dominant factor than a variation of subjects. This aspect of pose variation comes to the forein video surveillance, where a suspect may be pictured firstly frontal, whereas the correspondingtest images could be captured from the side, thus introducing a frontal-to-side recognition problem.Towards handling this problem, we employ multiple soft biometrics related traits. One ofour tasks here is to get some insight into the significance of these traits, specifically the significanceof using hair, skin and clothes patches for frontal-to-side re-identification. We are workingon the color FERET dataset [Fer11] with frontal gallery images for training, and side (profile)probe images for testing. Towards achieving re-identification, the proposed algorithm first analyzesthe color in Section 5.2.4.1 and furthermore texture in Section 5.2.4.2 of the three patches.Then we study the intensity correlations between patches in Section 5.2.4.3. This analysis is thenfollowed by the construction of a single, stronger classifier that combines the above measures inSection 5.2.5, to re-identify the person from his or her profile.Deviating from the above security related applications, we consider then an application closerto entertainment, and specifically consider the application of soft biometrics in analyzing andquantifying facial aesthetics.Chapter 6 - Soft biometrics for quantifying and predicting facial aestheticsWith millions of images appearing daily on Facebook, Picasa, Flickr, or on different socialand dating sites, photographs are often seen as the carrier of the first and deciding impression of aperson. At the same time though, human perception of facial aesthetics in images is a priori highlysubjective.We related among others soft biometric traits with this subjective human perception. In theprovided study we quantify insight on how basic measures can be used to improve photographsfor CVs or for different social and dating websites. This helps create an objective view on subjectiveefforts by experts / journalists when retouching images. We use the gained objective viewto examine facial aesthetics in terms of aging, facial surgery and a comparison of average femalesrelatively to selected females known for their beauty. Specifically in Section 6.3 we introducethe employed database, as well as describe the basic features and methods used in this study. InSection 6.4 we proceed with numerical results, and provide intuition on the role of features, imagequality and facial features, in human perception. In Section 6.5, we use these accumulated conclusionsto construct a basic linear model that predicts attractiveness in facial photographs usingdifferent facial traits as well as image properties. We then examine and validate the designed met-