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

79shown to have a high impact on our chosen beauty metric. We randomly selected 20 image pairs(before and after the surgery 2 , see Figure 6.7) from the plastic surgery database [SVB + 10], andafter annotation, we computed the related beauty indices. Interestingly our analysis suggested arelatively small surgery gain in the ̂MOS increase. Specifically the increase revealed a modestsurgery impact on the beauty index, with variations ranging in average between 1% and 4%.Figure 6.7: Examples of the Plastic Surgery Database. The left images depict the subjects beforesurgery, the right images after surgery.We proceed with the analysis and simulation of an automatic tool for facial beauty prediction.6.7 Towards an automatic tool for female beauty predictionAn automatic tool including classification regarding all 37 above presented traits will havethe benefit of a maximal achievable prediction score, at the same time though each automaticallydetected trait will bring an additional classification error into the prediction performance. Thus indesigning such an automatic tool a tradeoff between possible prediction score and categorizationerror has to be considered. We illustrate an analysis of prediction scores evoked by differentcombinations of traits in Table 6.2.Trait x iPearson’s correlationcoefficient r i,MOSx 1 0.5112x 1 , x 2 0.5921x 1 , x 2 , x 12 0.5923x 1 , x 2 , x 3 0.6319x 1 , x 2 , x 8 0.6165x 1 , x 2 , x 12 , x 15 0.5930x 1 , x 2 , x 3 , x 8 0.6502x 1 , x 2 , x 12 , x 15 , x 14 0.6070x 1 , x 2 , x 4 , x 12 , x 14 , x 15 0.6392x 1 , x 2 , x 4 , x 5 , x 12 , x 14 , x 15 0.6662x 1 , x 2 , x 4 , x 5 , x 12 , x 14 , x 15 , x 20 0.6711x 1 , x 2 , x 8 , x 14 , x 20 , x 23 0.6357Motivated by this Table 6.2 and towards simulating a realistic automatic tool for beauty prediction,we select a limited set of significant traits, x 1 ,x 2 ,x 8 , with other words factors describing1how big the eyes of a person 1 2 are, the ratio head width/head height and the presence of glasses.1 2 8Moreover we add acquisition traits with no extra error impact, such as x 14 ,x 20 ,x 23 , namely im-1 2 8 14 20 231age format, JPEG quality measure and image resolution. We then proceed to appropriate reliability1 21 2 8scores related tox 1 ,x 2 ,x 8 based on state of the art categorization algorithms:14 20 23 1 2 82. For this experiment all values attached to non permanent traits were artificially kept constant for "before surgery"and "after surgery" images.