Are attractive people symmetrical? Facial symmetry and perceptions of beauty

Barker College

Purpose: This paper aims to explain how quantitative measurements of facial ratios correlate to a qualitative perception of beauty/attractiveness. Do horizontally segmented symmetrical faces (with even distances between key features across the face) correlate with beauty? Design/methodology/approach: The project was completed using three photographs of each of the “most beautiful” 100 women (according to TC Candler’s ‘The 100 Most Beautiful Faces of 2020’). A correlation was performed on their beauty ranking from the list against a symmetry score as measured by the average standard deviation between the five segments of the face that have theoretically similar spacing: ear and outer eye; outer eye and inner eye; and the inner eye and inner eye (repeated for both sides of the face). Distances were measured using digital technology (Adobe Illustrator) Findings: There was no correlation between the qualitative ranking of beauty and the quantitative symmetry score (average standard deviation) of the five facial segments for these 100 women (Pearson’s r = 0.0235, R²= 0.0007). Research limitations/implications: As these women were from a dataset of those already deemed the most attractive, there may not be enough variation in attractiveness to demonstrate a clear correlation with symmetry. A comparison with "ordinary" faces was also completed which revealed a significant difference in symmetry (two-tailed t-test: t=-5.31452, p=<0.0001). This suggests a correlation over a greater beauty range could be considered. Practical implications: Symmetry is not a distinguishing factor of beauty amongst the top 100 “most beautiful” women, so highlighting or improving symmetry is an ineffective means of improving beauty amongst those already considered beautiful. Social implications: A lack of correlation may be comforting to some, however, the results found in this paper do suggest some relationship which requires further investigation. Originality/value: This paper, to the author’s knowledge, is the first comparison between nonscientifically ranked beauty and the quantitative study of facial ratios. Keywords: Facial symmetry, Beauty, Attractiveness Paper Type: Research paper

Pop culture is constantly promoting lists of those considered ‘attractive’ or ‘beautiful’, almost exclusively from a subjective standpoint, such as TC Candler’s ‘The 100 Most Beautiful Faces of 2020’ (Candler, 2020). The ranking of these women’s beauty is based on modern-day beauty standards, influenced by age, race, gender, ethnicity, and educational level of a person (Vent & Heppt, 2015). There is a question, however, of how objective the ranking could be if it was deconstructed. Beauty goes deeper than simply seeing the aesthetics of a person’s appearance. It was no accident that Darwin placed the conundrum of beauty under the heading of sexual selection (Darwin, 1871/2009). instinct to find a mate and reproduce (Gangestad & Scheyd, 2005; Hume & Montgomerie, 2001; Thornhill & Gangestad, 1999). The production of healthy offspring is a biological instinct that has accompanied human evolution. The human face is a key factor that people subconsciously use to determine whether the other person would be a possible mate: “the face displays secondary sexual characteristics, along with this, facial symmetry is more sensitive to environmental perturbations” (Wade, 2010).

Facial attractiveness can be accounted to different features, including: averageness, sexual dimorphism, youthfulness, and symmetry (Nguyen et al., 2016). The study of facial attractiveness has been measured specifically through symmetry in numerous different studies (described below).

Swaddle & Cuthill (1995) investigated the manipulation of female facial features in the form of (Figure 1): 1. Normal (no adjustments made from a photograph) 2. 25% transform of original (morphed original face with a 25% weighting of its mirror image) 3. Symmetrical (mirrored over the nose) 4. 25% transformation of mirrored 5. Inverted (right side becomes left side and vice versa)

These faces were shown to a sample of 52 university students, who subsequently ranked the faces according to attractiveness. The normal and mirror treatments were significantly more attractive than the symmetrical treatments, (t=3.72, p=0.0003)

Figure 1: Swaddle & Cuthill's study on the attractiveness of manipulated faces (Source: Swaddle & Cuthill, 1995)

Grammer & Thornhill (1994) created composite faces, by morphing multiple people’s faces together to make a more average face, and presented them to a test group. The testers were given a set of 23 pictures (16 original faces; four pictures combining four faces; two pictures combining faces; and one picture combining all 16 faces) and asked to use specific adjectives (pre-rated from 1-7) to describe the face. Female composites were judged significantly more attractive and sexier than normal photos, while normal male faces were rated as significantly healthier, sexier, and more dominant than composite male faces; the same pattern is seen for the adjective attractive (Figure 2). Included in these studies, is the theme of proposed objective rules or patterns have been quantify attractiveness. The definition of beauty has been explored since ancient Egyptian civilization. Euclid, Pythagoras, Vitruvius, and Leonardo Da, Vinci all tried to define beauty in mathematical algorithms. Albrecht Dürer (1471–1528) was also interested in the proportions of the human body and especially the facial profile and published sketches of human facial proportions in 1522 (see Figure 3). Potentially inspired by Dürer’s previous development, Kaya et al. (2019) used a 1/5’s ratio, splitting the face vertically into fifths, similarly to Figure 4, with each fifth created with two lines, plotted at key points on the face.

Figure 3: Facial profile studies of Albrecht Dürer (Source: Dürer,, 1528/1996)

Figure 2: Grammer & Thornhill's findings on the ratings of composite vs. normal faces (Grammer & Thornhill, 1994)

Figure 4: 1/5ths measurement (Source: Kaya et al., 2019)

This technique was used on 133 Turkish 18–40 year old patients. The face is divided into five parts in the vertical plane. The ideal width of the face is five times the width of the eye. (Kaya et al., 2019). The authors found that wide facial morphology (faces wider than ideal) was observed more than long facial morphology (faces longer than ideal). There was not a significant difference found between genders when compared to the ideal width ratio (1/5) - Suggesting that the 1/5ths ratio is applicable to both men and women.

Using the 1/5’s symmetry ratio to define beauty, Jovana Milutinovic, Ksenija Zelic, and Nenad Nedeljkovic (2014) comparison of ed models and actresses (some of which were named as the most beautiful and most proportional faces by the beauty and fashion magazines) and a random selection of 83 anonymous female university students. The authors found that the anonymous woman did not meet the parameters (1: 1.618) for the ideal face, with all of their results being statistically insignificant. For the models and actresses, the 1/5’s criterion was met in three out of the six parameters. Overall, the study showed a significant difference between the models/actresses and anonymous women, with the former more symmetrical and therefore more attractive.

This research paper aims to examine the link between subjective beauty rankings and empirical data on beauty. Following a similar method to Milutinovic et al, and corresponding to previous literature and knowledge, the hypothesis is that there will be a linear relationship between beauty rankings and symmetry. The comparison between ‘attractive’ people and the general population will solidify the findings of Miltutinovic et al, as well as confirm the hypothesis of this paper.

Does the quantitative measurement of the symmetry of a face using the 1/5’s ratio align with the qualitative ranking of TC Candler’s Top 100 Most Beautiful Faces of 2020?

An increase in the symmetry (using the 1/5ths ratio) will increase the subjective perception of beauty:

1. That the ranking of the beauty of TC Candler’s

Top 100 Most Beautiful Faces of 2020 will correlate with facial symmetry.

2. That the average symmetry of TC Candler’s

Top 100 Most Beautiful Faces of 2020 will be greater than the average symmetry of 100 randomly selected voice actresses and radio hosts, who are considered of more ‘regular’ beauty.

Three suitable photos of each of the people on TC Candler’s list were obtained using a simple online image search. This list was the ranking and names of the woman ranked 100-1 (Appendix 1) as determined by a panel of members who take part in the TC Candler page. For a photo to be used, it needed to: consist of a woman facing 'forward' towards the camera; be in the correct format (JPG or PNG); and be a of high enough quality for reasonable zooming (a minimum standard of a 100x100 pixel photo). The first three suitable and appropriate photos were saved. In the case of a photo that was suitable, but the head was tilted, the photo was rotated for the features to be ‘vertical’ for the measurement to take place.

The photos were then reviewed before the measuring process began; if there was a photo that had not met the standards, it was deleted a new image was found as a substitute to be used in measurement.

Figures 5, 6 and 7 are examples of the images taken for measurement.

Five women were omitted from the data sample for a variety of reasons:

• Rank 86 - Ariana Grande: Ariana has undergone plastic surgery, and there were insufficient acceptable photos of her either pre- or postsurgery to be used for measurement purposes. • Rank 82 – Ellie: Ellie is a 3D animated fictional character, and is not a biological entity. • Rank 64 - Dolorez Lorenzo: Dolorez an amateur model, there were insufficient acceptable photos of her available to be used for the experiment. • Rank 49 - Moa Sandell: Moa an amateur model, there were insufficient acceptable photos of her available to be used for the experiment. • Rank 7 - Harima Aden: Harima wears a hajib, which covers her ears, making them unable to be measured.

The correlation analysis would be performed on the 100 most beautiful faces. In addition, a comparison would be made with a control group of ‘regular’ faces. One hundred ‘regular’ faces were collected of voice actresses (acting as the voice for an animated character) and radio hosts from around the world (Appendix 2). These female faces represented the ‘regular’ population, as opposed to the TC Candler’s attractive population. These women were chosen through somewhat arbitrary means from suggested Google searches of ‘female radio hosts’ and ‘female voice actresses’. Once collected, the order of the woman was randomised to ensure no bias towards finding attractive people to measure first. The same photo standards and processes were used for this control group as the beautiful group.

The data collection process began once three photos of all 200 women (ie 100 beautiful; 100 control) had been obtained. For the analysis, Adobe Illustrator was used - a computer application that allows for precision measurements to be taken.

Figure 8: Illustration of the 1/5ths measurements taken on the face in Adobe Illustrator, done so on Meika Woolard (Rank 3)

In the application, measurements were taken using the process illustrated in Figure 8 (the 1/5’s version, as demonstrated in figure 4). Six lines were drawn, identifying the facial markers of the ears, inner and outer eye on both sides of the face. Once two lines were selected, under the properties tab, the measurement (W, as indicated by the red box in Figure 8) was taken as the distance between the two lines.

The process of producing a single number for both groups quantifying the facial symmetry was as follows:

1. The width of each segment was divided by the total width (from outer edge of ear to outer edge of other ear) of the face to determine the percentage of the total width. 2. The standard deviation of the five-width percentages was calculated. 3. Steps 1 and 2 were repeated for the second and third images. 4. The three standard deviations were averaged producing a ‘symmetry score’

This provided a quantified measure of facial symmetry, where a low average standard deviation indicated a high degree of symmetry.

A Pearson’s R correlation was completed comparing beauty rank with symmetry score.

A secondary analysis involved the control group of 100 ‘regular’ faces. Each face’s symmetry score was produced using the same method as for the ‘beautiful’ faces, and an independent t-test was used to compare the mean symmetry scores to see if the ‘beautiful’ faces were more symmetrical than the ‘regular’ faces.

Table 1 presents the symmetry score for the 100 faces on TC Chandler’s list. The data collected for each woman represents how closely they align with the 1/5ths ratio. A perfectly symmetrical face would result in a standard deviation of 0.0, as each segment would be exactly 20% of the face.

The blank boxed indicate the woman was removed from the sample, as explained in the method.

Table 1: Segment averages and symmetry of TC Candler’s Top 100 Most Beautiful Faces of 2020. The closer to zero for the symmetry score means the greater symmetry of the face.

# SEG 1 SEG 2 SEG 3 SEG 4

SEG 5 STDEV 100 22.1 19.2 18.2 18.8 21.7 1.8 99 20.2 18.6 22.1 18.4 20.6 1.5 98 17.6 16.6 22.5 17.9 25.5 3.8 97 20.4 23 21.9 18.9 15.8 2.8 96 21.4 21.4 20.5 19.3 17.4 1.7 95 19 17.4 21.7 16.7 25.3 3.5 94 24.1 19 20.2 14.7 22.1 3.5 93 17.8 20.3 22.1 20.4 19.5 1.6 92 16.4 18.5 21 19.6 24.6 3 91 24.9 19.1 19.6 18.3 18.1 2.8 90 20.8 18.8 18 19.6 22.8 1.9 89 22.8 17.7 21.5 17.2 20.9 2.5 88 18.1 22.2 18.8 20.5 20.4 1.6 87 17.7 16.8 19.3 18.4 27.8 4.5

85 21.5 17.2 18.9 16.6 25.7 3.7 84 24.4 17.1 19.7 17 21.8 3.2 83 19.7 23.2 19.3 19.1 18.7 1.8

81 17.3 20.5 23.2 18.9 20.1 2.2 80 20.6 18.3 19.4 18 23.8 2.3 79 20 17.6 20.2 17.4 24.7 2.9 78 23 17.5 18.7 17.6 23.2 2.9 77 21.5 17.1 19.6 17 24.9 3.3 76 21.1 18.6 21.6 17.3 21.4 1.9 75 18.9 21.7 20.6 20.1 18.8 1.2 74 22.6 14.9 21.1 14.6 26.7 5.2 73 22.2 19.9 20.7 17.8 19.4 1.6 72 21.7 16.2 20 16.2 25.9 4.1 71 18.2 17.9 23.2 17.8 22.9 2.8 70 25.8 18.7 17.8 16.9 20.9 3.6 69 20.3 16.9 21.5 17.2 24 3 68 20 18.8 20.9 17.3 23.1 2.2 67 19.6 18.2 18 17.2 27.1 4.1 66 20.2 19.1 20.5 16.9 23.2 2.3 65 17.3 19.5 18.7 19.5 25 2.9

63 19.4 17.6 18.2 21.3 23.6 2.4 62 14 22.1 21.7 20 22.2 3.4 61 18.8 19.8 21.9 18.8 20.8 1.3 60 25.7 17.4 19.8 18.1 19.1 3.3 59 18 19.6 23.1 19.5 19.7 1.9 58 18.1 18.7 21.8 19.5 21.9 1.7 57 18.6 19.4 18.3 19.9 23.9 2.3

56 19.4 20.5 24.1 18 17.9 2.5 55 20 17.9 18.7 18.2 25.3 3 54 19.6 18 23.7 17.6 21 2.5 53 23.9 18.4 20.5 18.3 18.9 2.3 52 20.5 19.3 21.6 19 19.6 1.1 51 22.9 17.3 17.2 18.5 24.1 3.3 50 21.4 17.3 21.6 17.4 22.4 2.5

48 23.8 17 20.5 16.3 22.4 3.3 47 21.7 20.8 22.4 20 15.1 2.9 46 17.1 18.7 21.7 18.7 23.8 2.7 45 20 19.5 19 20.3 21.1 0.8 44 24.8 19.2 19.2 18.7 18.1 2.7 43 19.4 18.9 20.8 16.1 24.7 3.2 42 23.3 17.8 20 17.6 21.3 2.4 41 25.7 18.9 19.5 18 17.9 3.3 40 20.4 17.8 21.1 18.7 22.1 1.7 39 22.9 19.5 18.5 18.5 20.5 1.8 38 19 16.9 19 18.5 26.7 3.8 37 22.4 16.2 21 17.6 22.9 3 36 19.2 20.2 21 19.5 20.1 0.7 35 22.5 18.1 20.5 16.8 22.1 2.5 34 21.3 17 20.1 17.4 24.2 3 33 21.1 18.3 19.6 17.7 23.3 2.3 32 19.5 19.2 22 18.5 20.8 1.4 31 23.3 18.1 22.4 16.7 19.6 2.8 30 18.1 20.4 21.5 20.9 19.1 1.4 29 20 17.6 20.3 18.1 24.1 2.6 28 21.9 20 20.6 19.4 18.2 1.4 27 25.6 16.1 22.7 16.1 19.5 4.2 26 19.6 19.1 19.8 19.1 22.4 1.4 25 18.1 18 19.6 19 25.3 3 24 20.9 18.3 19.8 18.6 22.3 1.6 23 20.6 16.5 21.4 16.8 24.8 3.5 22 19.6 19.4 23.7 20.1 17.2 2.4 21 19.9 17 21.1 17.5 24.5 3 20 19.3 18.5 21.3 18.9 22 1.5 19 19.4 17 18.9 17.9 26.9 4 18 18.7 17.1 21.5 16.6 26.1 3.9 17 20.1 18.5 19.7 18.1 23.6 2.2 16 21.9 15.8 20.1 17.7 24.5 3.4 15 20.8 16.3 21.9 17.9 23 2.8 14 18.5 17.8 20.9 18.6 24.2 2.6 13 21.4 16.8 19 17.4 25.4 3.5 12 19.4 17.3 21.8 17.8 23.7 2.7 11 23.4 18.7 20.1 18 19.7 2.1 10 19.3 16.5 21.9 17.3 25 3.5 9 20.8 19.4 22.5 18.5 18.8 1.7 8 22.5 17.3 21.8 17.3 21.1 2.5

6 21.1 19.4 24.7 16.9 17.9 3.1 5 19.1 17 22.2 17.9 23.8 2.9 4 21.5 17.3 22 16 23.2 3.1 3 22.5 16.5 20.6 16.4 24 3.5 2 18.1 17.9 21.3 18.2 24.5 2.9 1 19.6 19.9 19.7 19.6 21.1 0.6

From the data, a scatter plot was created, placing her on the graph according to her subjective ranking from TC Candler and her facial symmetry score (as measured by the average standard deviation).

Figure 9: Symmetrically of TC Candler’s 'The 100 most beautiful faces of 2020

A Person’s r correlation test was performed to relate the symmetry score to the ranking of the 100 most beautiful faces. It was found that there was almost no correlation between symmetry and the ranking of these faces (r = 0.0235).

The key research question of the report was to investigate whether the quantitative measurement of the symmetry of a face by the 1/5’s ratio aligns with the qualitative ranking of TC Candler’s The 100 Most Beautiful Faces of 2020? The testing had two groups: the ‘Attractive’ group, consisting of the top 100 ranked beautiful women, and the ‘regular’ group, which acts as a control, to confirm if the ‘attractive’ group represented a higher level of symmetrically than the average population.

For the ‘attractive’ group, the lack of correlation between the symmetry score (according to the 1/5ths ratio) and ranking on TC Candler’s list’ appears to contradict the first hypothesis and ideas presented in previous literature. There is a random spread of symmetry amongst these attractive faces. It must be cautioned, however, that this is a sample is one subset of women who are all highly attractive. While there is no correlation between symmetry and attractiveness amongst these women, that does not mean that no relationship between symmetry and attractiveness exists more broadly. If a wider and more broad range of faces was to be selected, it would be a more accurate representation of the hypothesis. The attractive group did not have any correlation between the subjective ranking and their symmetry. The symmetry data had a range of 4.6, with an average of 2.6. Out of the top 100, there was only one instance of a subjective ranking aligning with symmetry ranking – the first ranked person. With a symmetrical score of 0.6 she was the most symmetrical by only 0.1, compared to the 37th ranked woman, suggesting that symmetry did not play a role in the rankings.

The data produced an unexpected result of a lack of groupings in the symmetry scores of similarly ranked (numerically close) women. The expected result was that there would be higher instances of grouped results. There is a slight grouping around 24, 26, 28, 30, and 32 (with a range of 0.2), however, this is the only apparent grouping of similarly scoring women. This suggests randomness in the rankings, and that symmetry was not a key factor used to define beauty within TC Candler’s list.

The second hypothesis “that the average symmetry of TC Chandler’s Top 100 Most Beautiful Faces of 2020 will be greater than the average symmetry of 100 randomly selected voice actresses and radio hosts, who are considered of more ‘regular’ beauty” was supported by the data. A two-tailed t-test was completed to test whether the attractive population was significantly more symmetrical (tested using the means of the two data sets), and the result was significant (t=-5.31452, p=<0.0001). This finding suggests that the average population, represented by the ‘regular’ group, is less symmetrical than

Figure 10: Average symmetry and STDEV of attractive and regular faces

those who have been noted for their beauty. A visual analysis of figure 10 provides insight into how the attractive group can be perceived through the graph as more symmetrical, with the error bars (one standard deviation) having minimal overlap.

This study provides both consistencies and differentiating points from past literature. The difference between the symmetry of ‘attractive’ and ‘regular’ people aligns with the findings of Milutinovic et al. (2014). However, the findings of a relationships between symmetry and perceived attractiveness were not mirrored in this study. Swaddle & Cuthill (1995) found that regular, raw image faces are perceived more attractive than any of the composite symmetrical facial images. This paper’s findings differentiate from the ones in Swaddle & Cuthill (1995) as it implies that the findings should have a decreasing symmetricity score as the rankings increased – indicated by the raw images being perceived as more attractive than the symmetrical treatments. However, these were not the findings from the project.

An area for further research is a study comparing a wider survey of the ‘average’ population, which would improve the validity of the experiment. There may be an inherent bias in media (both voice acting and radio) around potential attractiveness, or more a more general bias that entices people who are deemed ‘attractive’ to be hired: “in mock interviews, attractive people [were] more likely to be hired than unattractive people” (Nelson Mail, 2017). A further area of study could include the same investigation, but a differing measurement to be taken from the faces – rather than the 1/5s measurement.

This research paper investigated whether, among the highest-ranked attractive woman, there was a correlation between their subjective ranking and a numerical value of their symmetricity, and as an extension, an exploration of whether they are more symmetrical than the average population. Symmetry was measured with the facial ratio of 1/5’s vertical segments, where the measurements were converted to percentages of the total face, producing a ratio of symmetry.

The data analysis resulted in no correlation between the subjective ranking of the woman, and their found symmetry, however, it did solidify the findings that ‘attractive’ people are more symmetrical than the ‘regular’ population.

Future studies could include an increasingly valid measurement of the ‘regular’ population or replication of the experiment using a different measurement of facial symmetry.

I would like to thank Dr. Matthew Hill for his continual and unfailing support of all aspects of my

project. This project would not have been possible without his advice and guidance in data analysis, and the education on the necessary means to complete my project.

I would also like to provide many thanks to Mr. Mark Onikul, for his support throughout the project in providing invaluable feedback on my writing; his assistance greatly improved the standard of my project.

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List of most attractive woman from TC Candler’s ‘Top 100 Most Beautiful Faces of 2020

1. Yael Shelvia : Israeli model 19 years old 2. LISA : Thai singer / dancer / model 23 years old 3. Meika Woolard : Australian model 16 years old 4. Tzuyu : Taiwanese singer / dancer 21 years old 5. Emily Neren : Norwegian Vlogger 25 years old 6. Thylane Blondeau : French model / actress 19 years old 7. 7th place: Harima Aden : Somalia Model /

Designer 23 years old 8. Nana : Korean actress / model / singer 29 years old 9. Josie Lane : British model 19 years old 10. Nancy Jewel Mcdonie : Korean / American singer / dancer 20 years old 11. Ivana Alawi : Philippines / Morocco Actress / Model / Singer 24 years old 12. YooA : Korean singer / dancer / model 25 years old 13. Naomi Scott : England actress 27 years old 14. Liza Soberano : Philippines / USA Actress /

Model 22 years old 15. Ella Balinska : American actress 24 years old 16. Lauren Tsai : USA / China Model /

Illustrator 22 years old 17. Anna Van Patten : American actress / model 22 years old 18. Kang Seulgi : Korean singer / dancer / actress 26 years old 19. Banita Sandhu : British / Indian actress 23 years old 20. Octabrina Maximova : Russian model 25 years old 21. Gal Gadot : Israeli model / actress 35 years old 22. Jourdan Dunn : British supermodel / actress / designer 30 years old 23. Jennie : Korean singer / dancer 24 years old 24. Kaylyn Slevin : American model / actress / dancer 20 years old 25. Maika Yamamoto: Japanese actress / model 23 years old 26. Hande Erçel : Turkish actress / model 27 years old 27. Zhou Dongyu : Chinese actress 28 years old 28. Dasha Taran : Russian model 21 years old 29. Sana Minatozaki (Sana): Japanese singer / dancer 23 years old 30. Sonia Ben Ammar : French model / actress / singer 21 years old 31. Rosé : New Zealand singer 23 years old 32. Jade Weber : France / Hong Kong / USA

Model 15 years old 33. Ana de Armas : Cuban actress 32 years old 34. Momo Hirai: Japanese singer / dancer / model 24 years old 35. Camilla Belle : American actress 34 years old 36. Lupita Nyongo : Mexico / Kenya Actress /

Model 37 years old 37. Sorn : Thai singer 24 years old 38. Emma Watson : British actress / model 30 years old 39. Dilraba Dilmurat : Chinese actress / model 28 years old 40. Karolina Pisarek : Polish model 23 years old 41. Urassaya Sperbund : Thailand / Norway 27 years old 42. Natalya Tsevelchugova: Russian model 20 years old 43. Ningning : Chinese singer / model / dancer 18 years old 44. Pooja Hegde : Indian actress / model 30 years old 45. Emilia Clarke : British actress 34 years old 46. Son Na-eun : Korean singer / actress / model 26 years old 47. Audreyana Michelle : American model 21 years old 48. Chaeryeong : Korean singer / dancer / model 19 years old 49. Moa Sandell: Swedish model 20 years old 50. Jisoo : Korean singer / actress / model 25 years old 51. Golshifteh Farahani : Iranian actress 37 years old 52. Vienna Maryce : Canadian model 16 years old 53. Seunghee: Korean singer / dancer / model 25 years old 54. Jassita Gurung : Nepalese actress / model 24 years old 55. Taylor Hill : American model 24 years old 56. Nana Komatsu: Japanese actress / model 24 years old 57. Franciny Ehlke : Brazilian model 21 years old

58. Jeon So-mi : Canada / Korean Singer /

Dancer 19 years old 59. Jasmine Tookes : American model 28 years old 60. Khin Wint Wah : Myanmar actress / model 26 years old 61. Chaewon: Korean singer / dancer / actress / model 23 years old 62. Germaine : Venezuelan singer / model 21 years old 63. Zhu Zhu: Chinese actress 36 years old 64. Dolorez Lorenzo : Argentine model 18 years old 65. Margot Robbie : Australian actress 30 years old 66. Chaeyoung : Korean rapper 21 years old 67. Lily Collins : British actress 31 years old 68. Angelina Danilova : Russian model / singer 24 years old 69. Satomi Ishihara: Japanese actress 34 years old 70. Natalie Portman : Israel / USA Actress /

Model / Director 39 years old 71. Taeyeon : Korean singer 31 years old 72. Chloe Grace Moretz : American actress 23 years old 73. Bettinah Tianah : Ugandan model / TV personality 27 years old 74. Lin Yun : Chinese actress 24 years old 75. Esra Birgichi : Turkish actress / model 28 years old 76. Solar : Korean singer / dancer / model 29 years old 77. Jena Frumes : American actress / model 26 years old 78. Valentina Sampaio : Brazilian model 24 years old 79. Jihyo : Korean singer / dancer / model 23 years old 80. Selena Gomez: American actress / singer 28 years old 81. Thao Nhi Le : Chinese model 26 years old 82. Erie : American game character 19 years old 83. Janice Joostema: Canadian model 24 years old 84. Gulnazar : Chinese actress / model 28 years old 85. Maudy Ayunda : Indonesian singer 26 years old 86. Ariana Grande : American singer 27 years old 87. Ju Jingyi : Chinese singer / actress 26 years old 88. Tarlan Parvaneh : Iranian actress 22 years old 89. Chungha : Korean singer / dancer / model 24 years old 90. Nathalie Emmanuel : British actress / model 31 years old 91. Priyanka Chopra : Indian actress 38 years old 92. Maralgua Dashnyam: Mongolian model 15 years old 93. Lina Qishawi : Palestine / Russia /

Kazakhstan TV presenter 27 years old 94. Marion Cotillard : French actress 45 years old 95. Nana Okada: Japanese idol 23 years old 96. Ivana Yturbe : Peruvian model / TV personality 24 years old 97. Ozgu Kaya: Turkish actress / model 24 years old 98. Song Yuqi : Chinese singer / dancer / model 21 years old 99. Anok Yai : Egyptian model 23 years old 100.Kate Beckingsale : British actress 47 years old

List of control ‘regular’ group woman. Assigned to an arbitrary number

1. Yolanda Adams 2. Em Rusciano 3. Debi Derryberry 4. Vick Hope 5. Laura Schlessinger 6. Adele Roberts 7. Kerri-Anne Kennerley 8. Kiki Sanford 9. Samantha Bee 10. Jackie O 11. Claudia Jordan 12. Jessica Amlee 13. Gillian Barber 14. Claudia Winkleman 15. Lucy Holmes 16. Alaina Burnett 17. Kate Garraway 18. Mary Kay Berg 19. Cherami Leigh 20. Donna Burke 21. Fifi Box 22. Emilie-Claire Barlow 23. Charlotte Arnold 24. Faith Salie 25. Nancy Cartwrig

26. Kazumi Evans 27. Sally Boazman 28. Caitlyn Bairstow 29. Cree Summer 30. Nadia Ali 31. Sarah-Jane Crawford 32. Russi Taylor 33. Jo Stanley 34. Lilly Bartlam 35. Jennifer Hale 36. Fearne Cotton 37. Yumi Stynes 38. Chloe Hollings 39. Arielle Free 40. Clare Balding 41. Cat Deeley 42. Susan Aceron 43. Angie Martinez 44. Meshel Laurie 45. Grey Griffin 46. Diane-Louise Jordan 47. Lucinda Cowden 48. Jane Kennedy 49. Amanda Keller 50. Tammy Bruce 51. Lisa Glasberg 52. Moira Stuart 53. Laura Bailey 54. Alice Levine 55. Tress MacNeill 56. Avani Dias 57. Carrie Bickmore 58. Mariella Frostrup 59. Soraya Azzabi 60. Amplify Dot 61. Robin Quivers 62. Jacqueline Brennan 63. Sarah Aubrey 64. Tracy Mann 65. Monica Rial 66. Alexis Stewart 67. Kathleen Turner 68. Christine Pedi 69. Chantelle Barr 70. Mairead Curran 71. Mel Greig 72. Helen Skelton 73. Lisa Ann Beley 74. Jennifer Bain 75. Chrissie Swan 76. Laura Ingraham 77. Delilah Rene 78. Kath Soucie 79. Lauren Laverne 80. B.Traits 81. Alanah Pearce 82. Kate Ritchie 83. Sugar :yn Beard 84. Kathryn Robinson 85. Wendy Harmer 86. Liz Kershaw 87. Heather Bambrick 88. Kari Wahlgren 89. Wendee Lee 90. Myf Warhurst 91. Maya Jama 92. Claudia Black 93. Sara Cox 94. Angie Greaves 95. Ashleigh Ball 96. Elizabeth Daily 97. Anna Galvin 98. Clara Amfo 99. Emily Browning 100.Terry Gross