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| Computer Taught To Recognize Attractiveness In Women |
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| SciMed - Neuroscience | |||
| TS-Si News Service | |||
| Thursday, 10 April 2008 18:00 | |||
Tel Aviv, Israel. Does the beholder of beauty have to be human? Not necessarily, say scientists who successfully taught a computer to interpret female attractiveness. But, if we can teach a computer to recognize beauty, what rules do we use? Perceptions about beauty and attractiveness can vary across cultural, racial and ethnic groups. Very often, there can be fierce disagreements within those groups. Moreover, females and — especially — males can have strong opinions on the subject.
But there's a more serious dimension to this issue that reaches beyond mere vanity. The discovery by scientists at Tel Aviv University (TAU) is a step towards developing artificial intelligence in computers. Other applications for the software could be in plastic and reconstructive surgery and computer visualization programs such as face recognition technologies.
A machine learning predictor of facial attractiveness revealing human-like psychophysical biases. Amit Kagian, Gideon Dror, Tommer Leyvand, Isaac Meilijson, Daniel Cohen-Or and Eytan Ruppin. Vision Research, 48(2), January 2008, Pages 235-243. doi: 10.1016 / j.visres.2007.11.007 From Mathematics to Aesthetics
 Current study limited to caucasian women. 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.
Beauty is Golden
"Personally, I believe that some kind of universal correctness to beauty exists in nature, an aesthetic interpretation of the universal truth. But because each of us is trapped with our own human biases and personalized viewpoints, this may detract us from finding the ultimate formula to a complete understanding of beauty." Kagian noted that "… Plato connected the good to the beautiful."
Kagian, who studied under the Adi Lautman multidisciplinary program for outstanding students at Tel Aviv University (TAU), says that a possible next step is to teach computers how to recognize "beauty" in men. This may be more difficult. Psychological research has shown that there is less agreement as to what defines "male beauty" among human subjects. And his own portrait, jokes Kagian, will not be part of the experiment.
"I would probably blow up the machine," he says.
Co-authors on the work were Kagian's supervisors Prof. Eytan Ruppin and Prof. Gideon Dror. A machine learning predictor of facial attractiveness revealing human-like psychophysical biases. Amit Kagian, Gideon Dror, Tommer Leyvand, Isaac Meilijson, Daniel Cohen-Or and Eytan Ruppin. Vision Research, 48(2), January 2008, Pages 235-243. doi: 10.1016 / j.visres.2007.11.007 Abstract. Recent psychological studies have strongly suggested that humans share common visual preferences for facial attractiveness. Here, we present a learning model that automatically extracts measurements of facial features from raw images and obtains human-level performance in predicting facial attractiveness ratings. The machine’s ratings are highly correlated with mean human ratings, markedly improving on recent machine learning studies of this task. Simulated psychophysical experiments with virtually manipulated images reveal preferences in the machine’s judgments that are remarkably similar to those of humans. Thus, a model trained explicitly to capture a specific operational performance criteria, implicitly captures basic human psychophysical characteristics.
 The TS-Si News Service is a collaborative effort by TS-Si.org editors, contributors, and corresponding institutions. Sources can include the cited individuals and organizations, as well as TS-Si.org staff contributions. Articles and news reports do not necessarily convey official positions of TS-Si, its partners, or affiliates. We welcome your comments. Use the form below to leave a public comment or send private correspondence via the TS-Si Contact Page. We will not divulge any personal details or place you on a mailing list without your permission.TS-Si is dedicated to the acceptance, medical treatment, and legal protection of individuals correcting the misalignment of their brains and their anatomical sex, while supporting their transition into society as hormonally reconstituted and surgically corrected citizens.
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| Last Updated on Thursday, 10 April 2008 17:37 |
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The TS-Si News Service
and the TS-Si Research Service are collaborations of TS-Si officials, staff, contributors, and corresponding institutions. The contents do not necessarily convey official positions of TS-Si or its owners, participants, partners, or affiliates.