Computation of a face attractiveness index based on neoclassical canons, symmetry, and golden ratios
Introduction
A popular axiom concerning physical attractiveness is: “Beauty is in the eye of the beholder”. Research in the area of facial perception has identified many different factors that contribute to a face being considered attractive. Armstrong [1] suggests that beauty cannot be defined by one single principle. Rhodes [2] focuses on averageness, symmetry, and sexual dimorphism and their link to facial attractiveness. Little et al. [3] suggest that self-perceived attractiveness influences one's opinion of the attractiveness of others, and DeBruine [4] shows both males and females prefer faces that resemble their own.
In this paper, we develop a quantitative method for measuring facial attractiveness using a combination of several factors that have been deemed significant in previous research. Many previous studies have used composite faces or faces that are altered in some other way to study the effects of symmetry and averageness on attractiveness [2], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16]. In contrast, we use the actual faces compiled from a standard face recognition database for our analysis as the process of averaging faces for composites can diminish the appearance of attributes that would classify a face as more or less attractive [17]. We then determine the location of important landmarks in the face [18], [19]. In all, 29 landmarks on each face as described by Shi et al. [20] are used to take physical measurements and compute the values of three factors: neoclassical canons, symmetry, and golden ratios. The faces are presented to a set of human subjects to determine their perceived attractiveness to find which factor or which combination of factors is the best predictor of attractiveness. We systematically investigate the relationship between a face's measurements and its attractiveness. The features that play the greatest role in attractiveness are identified for both genders of raters and faces. In addition, the way males and females view attractiveness in faces of the same and opposite gender is explored as there are differing accounts in the literature. Perrett et al. [12] show that males and females prefer caricatured composite faces to average composite faces in both male and female images. O’Toole et al. [17] find that females rate female faces significantly higher than they rate male faces and that the femininity of females is strongly related to attractiveness. Full details of this research can be found in Ref. [21].
Section snippets
Datasets and experimental design
We begin with an image database containing a set of face images for the experiment and analysis. Using the image database, two datasets are compiled for the analysis. The feature dataset consists of the locations of the landmarks in the faces. The attractiveness dataset contains the attractiveness ratings given to the images by the human participants.
Computation of attractiveness predictors
The main motivation of this research is to examine the attractiveness of a face, , as a function of its face geometry captured by a set of m landmarks. Thus: where each feature point, , , , is represented by its two-dimensional spatial coordinates in the face. The goal is to determine a function A that maps a face to an attractiveness score. To compute the attractiveness, we use three predictors that have been proposed in literature:
Analyses and results
We begin with the examination of a set of general questions about the attractiveness of human faces, including the variability of raters and effect of self-perceived attractiveness. Then the roles of the three predictor variables, neoclassical canons, symmetry, and golden ratio, in the attractiveness of a face are examined. All analyses use the statistical analysis software (SAS) [33].
Summary and future work
The goal of this study is to determine a predictive model for attractiveness based on neoclassical canons, symmetry, and golden ratios. In contrast with much of the previous work, our study used landmarks and geometry based means for computing symmetry and had people rate actual faces instead of composite or altered faces. We also include both faces of the general population and known attractive faces. In addition, both the gender of the rater and the face are identified as to compare the
About the Author—KENDRA SCHMID obtained a M.S. (2004) and Ph.D. in Statistics (2007) from the University of Nebraska-Lincoln. Besides her interests in pattern and shape analysis and statistics education, her research has involved statistical computing and nonlinear models. She is an Assistant Professor of Biostatistics at the University of Nebraska Medical Center.
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About the Author—KENDRA SCHMID obtained a M.S. (2004) and Ph.D. in Statistics (2007) from the University of Nebraska-Lincoln. Besides her interests in pattern and shape analysis and statistics education, her research has involved statistical computing and nonlinear models. She is an Assistant Professor of Biostatistics at the University of Nebraska Medical Center.
About the Author—DAVID MARX is a Professor of Statistics at the University of Nebraska-Lincoln. He obtained his B.S. in Chemistry from the College of Wooster in Ohio, M.S. and Ph.D. in Statistics from the University of Missouri and the University of Kentucky, respectively. His research interests include spatial statistics, geostatistics, and statistical computing.
About the Author—ASHOK SAMAL received his B.Tech. in Computer Science from Indian Institute of Technology and Ph.D. from the University of Utah. He is an Associate Professor with the Department of Computer Science and Engineering at the University of Nebraska-Lincoln. He has published over 70 papers in image understanding, document analysis, and geospatial computing.