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Amit Kagian, an M.Sc. graduate from the TAU School of Computer Sciences, has successfully “taught” a computer how to interpret attractiveness in women. Kagian published the findings in the scientific journal Vision Research. Co-authors on the work were Kagian’s supervisors Prof. Eytan Ruppin and Prof. Gideon Dror. The study combined the worlds of computer programming and psychology, an example of the multidisciplinary research for which TAU is world-renowned.Cool. Soon modeling agencies will be able to sort through thousands of photo submissions quickly, looking for the attractive ones. Webstalkers will be able to program bots to troll myspace and other social networking sites for attractive people to harass. Will wonders never cease?
In the first step of the study, 30 men and women were presented with 100 different faces of Caucasian women, roughly of the same age, and were asked to judge the beauty of each face. The subjects rated the images on a scale of 1 through 7 and did not explain why they chose certain scores. Kagian and his colleagues then went to the computer and processed and mapped the geometric shape of facial features mathematically.
Additional features such as face symmetry, smoothness of the skin and hair color were fed into the analysis as well. Based on human preferences, the machine "learned" the relation between facial features and attractiveness scores and was then put to the test on a fresh set of faces.
Says Kagian, "The computer produced impressive results its rankings were very similar to the rankings people gave." This is considered a remarkable achievement, believes Kagian, because it’s as though the computer “learned” implicitly how to interpret beauty through processing previous data it had received.