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

80 6. SOFT BIOMETRICS FOR QUANTIFYING AND PREDICTING FACIAL AESTHETICS– facial landmark recognition with accuracy of of 6.23 pixels and 2.1%, reported in the work[DM08],– face localization with accuracy between90% and98% depending on the database presentedin [GLW + 11] and– glasses detection with accuracy of94% shown in [WAL04].We deteriorate the manually annotated data with the above realistic algorithmic estimationaccuracies and compute the Pearson’s correlation coefficient between user MOS rating and thepredicted ̂MOS based on simulated error prone algorithms. We obtain a realistic simulated beautyprediction performance presented in Table 6.3. Such an automatic tool, based only on three traitsprovides related results that would outperform outcomes from Eigenfaces of r̂MOS,MOS) = 0.18(see [GKYG10]) and neural networks = 0.458 (see [GKYG10]).r̂MOS,MOSCombined Traits x iPearson’s correlationcoefficient r i,MOSx 1 0.5112x 1 , x 2 0.5921x 1 , x 2 , x 8 0.6165x 1 , x 2 , x 8 , x 14 , x 20 , x 23 0.6357Degraded x 1 0.4927Degraded x 1 , x 2 0.5722Degraded x 1 , x 2 , x 8 0.5810x 14 , x 20 , x 23 and degraded x 1 , x 2 , x 8 0.60076.8 SummaryIn this chapter, we presented a study on facial aesthetics in photographs, where we comparedobjective measures (namely photograph quality measures, facial beauty characteristics and softbiometrics), with human subjective perception. Our analysis revealed a substantial correlation betweendifferent selected traits, and the corresponding MOS-related beauty indices. Specificallywe presented that non permanent features can influence highly the MOS, and based on our analysiswe conclude that facial aesthetics in images can indeed be substantially modifiable. Withother words parameters such as the presence of makeup and glasses, the image quality as wellas different image post–processing methods can significantly affect the resulting MOS. Furthermorewe constructed a linear MOS–based metric which was successfully employed to quantifybeauty-index variations due to aging and surgery. Our work applies towards building a basis fordesigning new image-processing tools that further automate prediction of aesthetics in facial images.Towards this we provided a simulation of an automatic prediction tool based on state of theart categorization algorithms and the designed MOS–prediction metric.By now we ensured the user of the practicality of SBS for security as well as entertainmentapplications. In a next step we provide a chapter 7 featuring classification algorithms of a SBS, asemployed and analyzed in the chapters 3 and 4.