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

12person. 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 photographs forCVs 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 view toexamine facial aesthetics in terms of aging, facial surgery and a comparison of average femalesrelatively to selected females known for their beauty. Specifically we provide intuition on therole of features, image quality and facial features, see Figure 5, in human perception. We usethese accumulated conclusions to construct a basic linear model that predicts attractiveness infacial photographs using different facial traits as well as image properties. We then examineand validate the designed metric. We employ the developed metric to conduct experiments andanswer questions regarding the beauty index in three cases: for famous attractive females, foraging females and in case of facial surgery. Finally we proceed to simulate, based on both, thepresented metric, as well as state of the art algorithmic accuracies an automatic tool for beautyprediction.Figure 5: Example image of the web site HOTorNOT,MOS = 9.8. The white disks represent thestress points, the red cross the image center.Practical implementation of soft biometrics classification algorithmsTowards practical implementation of the related concepts and ideas we develop a tool (concatenationof classification algorithms) for classification of facial soft biometric traits, where wespecifically emphasize on the most obvious facial identifiers, primarily mentioned by humans,when portraying an unknown individual. The constructed tool is streamlined to achieve reliabilityof identification at reduced complexity, and hence focuses on simple yet robust soft-biometrictraits, including hair color, eye color and skin color, as well as the existence of beard, moustacheand glasses, see Table 2. We then specifically focus on extraction and categorization of eye color,and present an additional study where we illustrate the influence of surrounding factors like illumination,eye glasses and sensors on the appearance of eye color.We create based on those traits a bag of six facial soft biometrics, see Figure 6, for which