A Quantitative Approach to Beauty. Perceived Attractiveness of Human Faces in World Painting

Heterogeneity in chromatic distance in images and characterization of massive painting data set

“…Venturing further, non-western European or American art including Asian, Hindu, and Islamic painting art have been largely untouched in our work; large-scale analyses of these subjects would also be of immediate, universal interest. Also, integrating an analytical study using stylometric measures such as ours with object detection and segmentation techniques from machine learning could lead to a deeper understanding of art that incorporates both the styles and contents of paintings [26,45,46]. For example, how the same objects or motifs have been por- trayed differently over time would shed light on changes in tastes as well as style.…”

“…A recent development that is proving to have far-reaching implications for a scientific exploration of human actions and behavior in many social, cultural complex systems is the increasing availability of massive high-quality data that allows a large-scale application of scientific frameworks and verifi-cation [2][3][4][5][6][7]. In the area of culture, subjects on which quantitative pattern-finding have been performed to a degree include literature [8][9][10][11][12] where Polish linguist Wincenty Lutosławski's work on the statistical features of word usage in Plato's Dialogue [8] is well known, music [13][14][15][16][17], and painting [18][19][20][21][22][23][24][25][26]. A landmark scientific study of paintings can be found in Taylor et al's characterisation of Jackson Pollock's drip paintings using fractal geometry to distinguish between authentic Pollocks and those of unknown origins [18], demonstrating that an artistic style can be quantified.…”

“…More recent examples regarding painting include Lyu et al's wavelet-based decomposition of images [20], Hughes et al's sparse-coding models for authenticating artworks [21], Kim et al's characterization of variations in chiaroscuro technique via the so-called "roughness exponent" from statistical physics [22] and Gatys et al's style representation derived from correlations between the different features in different layers in a Convolutional Neural Network [23]. Besides quantification of artistic styles, some studied perceived similarities between different paintings [24], the influence relationships between artworks for quantifying creativity in an artwork [25], and the changes in the perception of beauty using face-recognition on images from different eras [26].…”