Evolving attractive faces using morphing technology and a genetic algorithm: a new approach to determining ideal facial aesthetics.
Objectives: The objectives of this study were to: 1) determine if a genetic algorithm in combination with morphing software can be used to evolve more attractive faces; and 2) evaluate whether this approach can be used as a tool to define or identify the attributes of the ideal attractive face.Study...
| Publicado en: | Laryngoscope Vol. 118; no. 6; pp. 962 - 975 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2008
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105780370&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105780370 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0023852X 1GR jtl: Laryngoscope issn: 0023852X maglogo: Y pubinfo: dt: 2008 vid: 118 iid: 6 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 105780370 NLM18401273 2009944630 10.1097/MLG.0b013e31816bf545 NLM18401273 PMC3786677 105780370 ppf: 962 ppct: 13 formats: tig: atl: Evolving attractive faces using morphing technology and a genetic algorithm: a new approach to determining ideal facial aesthetics. aug: au: Wong BJF Karimi K Devcic Z McLaren CE Chen W Wong, Brian J F Karimi, Koohyar Devcic, Zlatko McLaren, Christine E Chen, Wen-Pin affil: Department of Otolaryngology-Head and Neck Surgery, University of California Irvine, Orange, California 92812, USA sug: subj: Aesthetics Face Anatomy and Histology Genetics, Medical Adolescence Adult Algorithms Female Software Adolescent: 13-18 years Adult: 19-44 years Female ab: Objectives: The objectives of this study were to: 1) determine if a genetic algorithm in combination with morphing software can be used to evolve more attractive faces; and 2) evaluate whether this approach can be used as a tool to define or identify the attributes of the ideal attractive face.Study Design: Basic research study incorporating focus group evaluations.Methods: Digital images were acquired of 250 female volunteers (18-25 y). Randomly selected images were used to produce a parent generation (P) of 30 synthetic faces using morphing software. Then, a focus group of 17 trained volunteers (18-25 y) scored each face on an attractiveness scale ranging from 1 (unattractive) to 10 (attractive). A genetic algorithm was used to select 30 new pairs from the parent generation, and these were morphed using software to produce a new first generation (F1) of faces. The F1 faces were scored by the focus group, and the process was repeated for a total of four iterations of the algorithm. The algorithm mimics natural selection by using the attractiveness score as the selection pressure; the more attractive faces are more likely to morph. All five generations (P-F4) were then scored by three focus groups: a) surgeons (n = 12), b) cos-metology students (n = 44), and c) undergraduate students (n = 44). Morphometric measurements were made of 33 specific features on each of the 150 synthetic faces, and correlated with attractiveness scores using univariate and multivariate analysis.Results: The average facial attractiveness scores increased with each generation and were 3.66 (+0.60), 4.59 (+/-0.73), 5.50 (+/-0.62), 6.23 (+/-0.31), and 6.39 (+/-0.24) for P and F1-F4 generations, respectively. Histograms of attractiveness score distributions show a significant shift in the skew of each curve toward more attractive faces with each generation. Univariate analysis identified nasal width, eyebrow arch height, and lip thickness as being significantly correlated with attractiveness scores. Multivariate analysis identified a similar collection of morphometric measures. No correlation with more commonly accepted measures such as the length facial thirds or fifths were identified. When images are examined as a montage (by generation), clear distinct trends are identified: oval shaped faces, distinct arched eyebrows, and full lips predominate. Faces evolve to approximate the guidelines suggested by classical canons. F3 and F4 generation faces look profoundly similar. The statistical and qualitative analysis indicates that the algorithm and methodology succeeds in generating successively more attractive faces.Conclusions: The use of genetic algorithms in combination with a morphing software and traditional focus-group derived attractiveness scores can be used to evolve attractive synthetic faces. We have demonstrated that the evolution of attractive faces can be mimicked in software. Genetic algorithms and morphing provide a robust alternative to traditional approaches rooted in comparing attractiveness scores with a series of morphometric measurements in human subjects. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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