The study of human attractiveness with pattern analysis techniques is an emerging research field. One still largely unresolved problem is which are the facial features relevant to attractiveness, how they combine together, and the number of independent parameters required for describing and identifying harmonious faces. In this paper, we present a first study about this problem, applied to face profiles. First, according to several empirical results, we hypothesize the existence of two well separated manifolds of attractive and unattractive face profiles. Then, we analyze with manifold learning techniques their intrinsic dimensionality. Finally, we show that the profile data can be reduced, with various techniques, to the intrinsic dimensions, largely without loosing their ability to discriminate between attractive and unattractive faces.

The Intrinsic Dimensionality of Attractiveness: A Study in Face Profiles / Bottino, ANDREA GIUSEPPE; Laurentini, Aldo. - STAMPA. - 7441:(2012), pp. 59-66. (Intervento presentato al convegno 17th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2012 tenutosi a Buenos Aires (Argentina) nel 3-6 September 2012) [10.1007/978-3-642-33275-3_7].

The Intrinsic Dimensionality of Attractiveness: A Study in Face Profiles

BOTTINO, ANDREA GIUSEPPE;LAURENTINI, ALDO
2012

Abstract

The study of human attractiveness with pattern analysis techniques is an emerging research field. One still largely unresolved problem is which are the facial features relevant to attractiveness, how they combine together, and the number of independent parameters required for describing and identifying harmonious faces. In this paper, we present a first study about this problem, applied to face profiles. First, according to several empirical results, we hypothesize the existence of two well separated manifolds of attractive and unattractive face profiles. Then, we analyze with manifold learning techniques their intrinsic dimensionality. Finally, we show that the profile data can be reduced, with various techniques, to the intrinsic dimensions, largely without loosing their ability to discriminate between attractive and unattractive faces.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2502048
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