Yael Eisenthal

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This work presents a novel study of the notion of facial attractiveness in a machine learning context. To this end, we collected human beauty ratings for data sets of facial images and used various techniques for learning the attractiveness of a face. The trained predictor achieves a significant correlation of 0.65 with the average human ratings. The(More)
School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel eisentha@post.tau.ac.il, ruppin@post.tau.ac.il Department of Computer Sciences, Academic College of Tel-Aviv-Yaffo, Tel-Aviv 64044, Israel gideon@mta.ac.il DRAFT (03/06/2004) Abstract In this work we study of the notion of “attractiveness” of faces in a machine-learning context. To this(More)
Our project was a continuation of the recent research published by Yael Eisenthal, Gideon Dror, and Eytan Ruppin entitled: Facial Attractiveness: Beauty and the Machine [1]. The authors used a variety of machine learning techniques to predict facial attractiveness ratings from photographs. They had two data sets, each consisting of ninety-two photographs of(More)
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