Artists portray human faces with the Fourier statistics of complex natural scenes
- 1 January 2007
- journal article
- Published by Taylor & Francis in Network: Computation in Neural Systems
- Vol. 18 (3) , 235-248
- https://doi.org/10.1080/09548980701574496
Abstract
When artists portray human faces, they generally endow their portraits with properties that render the faces esthetically more pleasing. To obtain insight into the changes introduced by artists, we compared Fourier power spectra in photographs of faces and in portraits by artists. Our analysis was restricted to a large set of monochrome or lightly colored portraits from various Western cultures and revealed a paradoxical result. Although face photographs are not scale-invariant, artists draw human faces with statistical properties that deviate from the face photographs and approximate the scale-invariant, fractal-like properties of complex natural scenes. This result cannot be explained by systematic differences in the complexity of patterns surrounding the faces or by reproduction artifacts. In particular, a moderate change in gamma gradation has little influence on the results. Moreover, the scale-invariant rendering of faces in artists' portraits was found to be independent of cultural variables, such as century of origin or artistic techniques. We suggest that artists have implicit knowledge of image statistics and prefer natural scene statistics (or some other rules associated with them) in their creations. Fractal-like statistics have been demonstrated previously in other forms of visual art and may be a general attribute of esthetic visual stimuli.Keywords
This publication has 27 references indexed in Scilit:
- Statistical regularities of art images and natural scenes: Spectra, sparseness and nonlinearitiesSpatial Vision, 2008
- Image statistics of American Sign Language: comparison with faces and natural scenesJournal of the Optical Society of America A, 2006
- The artist as neuroscientistNature, 2005
- Fractal dimension of landscape silhouette outlines as a predictor of landscape preferenceJournal of Environmental Psychology, 2004
- Sparse coding of natural contoursNeurocomputing, 2002
- From few to many: illumination cone models for face recognition under variable lighting and posePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2001
- Occlusion Models for Natural Images: A Statistical Study of a Scale-Invariant Dead Leaves ModelInternational Journal of Computer Vision, 2001
- Independent component filters of natural images compared with simple cells in primary visual cortexProceedings Of The Royal Society B-Biological Sciences, 1998
- Natural image statistics and efficient codingNetwork: Computation in Neural Systems, 1996
- Relations between the statistics of natural images and the response properties of cortical cellsJournal of the Optical Society of America A, 1987