Printer Friendly

Correlation of measured intelligence and facial morphology among the selected students of Mindanao State University-Iligan Institute of Technology, Iligan City, Philippines.


The human face is an intricate structure with crucial social functional cues [1] and is strongly influenced by genetic factors evident by the study of twins [2]. Faces inform us about personality [3-5], sex and age [6], sexual orientation [7], health status [8-10], ethnicity [11], social rank [12], attractiveness [13-15] and political affiliation [16]. Kluckhohn and Murray [17] has led to the search for key individual difference factors that can be operationally defined and measured. Individual differences characteristics attributed as the cause of underlying basis of human behavior include intelligence, personality and conative factors [18]. Significantly, the use of the human face as a complex semantic organ, which among others reflects human individuality due to the high variance of facial morphologies can be used to analyze visual cues to the intelligence of a person [19, 15]. Though several varied mammalian species exhibit well-developed facial structures, the communicative and expressive roles of the face reach a unique level of ability in human beings [20]. Consequently, the ability to accurately assess the intelligence of other persons finds its place in everyday social interaction and should have important evolutionary consequences [1].

Few studies correlated facial morphology in the intelligence quotient (IQ) of the person. With the advancement in geometry, biology, psychology, computer science and statistics, morphological analysis however, information on inter-individual morphological variation among human populations were quantitatively described [10,11,21,22]. The advent of next-generation landmark-based geometric morphometrics (GM) methods especially relative warp analysis determining variation of different populations using these tools have become very useful [23-25]. More powerful morphometric analyses can be performed using these more comprehensive data [26, 27] and very subtle shape differences can be visualized [23, 28]. The landmark-based geometric morphometric methods provide new insights into patterns of biological shape variation that could not be evaluated by traditional methods [28-32]. With these theoretical and empirical reasons, geometric morphometries was considered a good tool in determining morphological variations among IQ groups in selected students in the university. Although a number of studies have examined the perception of intelligence from different visual cues, none of these studies describes the specific facial traits that play a role in intelligence assessment thus this study was conducted. Classifying the physical appearances of unknown people helps us to navigate the social world. As they are prominently displayed, facial traits play a particularly important role in social perception. Facial appearance influences our perception of broad social categories and it can provide cues to some less obvious categories such as intelligence. The findings of the current study provide baseline information if facial traits can be associated with measured intelligence.


A total of 170 (68 males and 102 females) freshmen students from the College of Science and Mathematics were allowed to take the Psychology Test (IQ testing) administered by the Guidance and Counselling Center (GCC) of Mindanao State University-Iligan Institute of Technology. A letter for informed consent were signed by students who volunteered to participate in the study. The data on measured intelligence were analyzed anonymously and confidentially. To measure the intelligence of the subjects, the Psychology Test using Culture Fair Intelligence Test (CFIT), Scale 2 was administered to the respondents. This test measures individual intelligence in a manner designed to reduce, as much as possible, the influence of verbal fluency, cultural climate, and educational level. CFIT is non-verbal and require only that examinees perceive relationships in shapes and in figures which may be administered individually or in group. Each scale contains subtests, involving different perceptual tasks, so that the composite intelligence measure avoids spurious reliance on a single skill. In this study, Scale 2 was used which consists of four subtests namely, Series, Classifications, Matrices and Conditions which is wholly group-administrable. In the first subtest, Series, the individual was presented with an incomplete, progressive series to select among the choices provided which best continues the series. In the next subtest, Classification, individual was presented with five figures and must select one which is different from the other four. The Matrices subtest, however, involves task to correctly complete the design or matrix presented at the left of each row. Finally, the subtest Conditions (or Topology) required the individual to select, from the five choices provided, the one which duplicates the conditions given in the far left box. All coefficients for this structured test were quite respectably high and have been evaluated across large and widely diverse samples.

Photographs of 68 males and 102 females were taken. The student respondents were grouped accordingly into six (6) IQ categories based on the remarks of their psychology test results namely, below average (BA), average (A), above average (AA), high (H), very high (VH) and superior (S). The students were seated in front of a white background and were photographed with a digital camera. They were instructed to adopt a neutral, non-smiling expression and avoid facial cosmetics, jewelry, and other decorations. All of them were informed the need to use headband to capture the facial landmarks on the midpoint of the hairline to measure the size of the forehead. The photos were cropped to place the eyes horizontally at the same height and leave a standard length of neck visible.

Forty-three (43) anatomical landmarks were marked in areas that illustrate the morphological variations (Table 1, Fig. 1). Landmarking of the digitized images was done in triplicates and the Cartesian coordinate scores of these landmarks were recorded using the tpsDig, ver. 1.40 [33]. The landmark sites in the face are illustrated in Fig. 1. Each landmark was classified into type I landmark, a point that occurs at joints of tissues or bones, or type II landmark, a point defined by local properties such as maximal curvatures [34]. Landmark data were used to determine differences within and between male and female shape of the face. From the pooled data between and within male and female populations as grouped in each IQ categories, respectively, the calculation of variables, such as consensus, partial warps, and relative warps, which describe variation in shape, was done by using the tpsRelw, ver. 1.46 program [35]. The relative warp scores obtained were used for the generation of histograms and boxplots using the Paleontological Statistics software (PAST) version 3.04 [36].

To observe the variation among the landmark data configurations of all specimens, Multivariate Analysis of Variance (MANOVA) was employed to observe the Canonical Variates Analysis (CVA) scatter plot and significant difference using PAST 2.17. Further visualizations of variation for both male and female population and within male and female groups were presented using histograms and boxplots. The Kruskal-Wallis test, a nonparametric test used to compare independent groups of sampled data was done using PAST 3.04. Correlation analysis was employed to regress face morphology onto scores of intelligence rating or IQ by using bivariate linear regression in which the dependent variable was the relative warp scores generated from tpsRelw, ver. 1.46 program [35] and the independent variable was the intelligence rating or IQ scores. The r value or the coefficient of determination and p value were obtained to determine if there is correlation between the relative warp scores of RW scores and IQ scores.


Results of the evaluation of IQ of the 170 students who participated in this study are shown in Table 2. Only very few students are shown to belong to have superior IQ while most of those evaluated have above average (AA) IQ scores. The face of these students were further studied to be able to determine if there exist a correlation between the shape of the face and IQ scores.

CVA analysis of relative warp scores to determine variations in facial shapes between male and female students with different IQ's was done (Table 4). The distribution of individuals with determined facial shapes are visualized along a scatterplot (Fig. 2). The Descriptions of the shapes of the individuals are shown in Table 3 and graphically illustrated in Figures 3 and 4.

Results showed significant differences in the Canonical Variates Analysis of relative warp scores (Table 4). The variations are shown in the CVA plots. While there were overlaps, the results still indicate the differences between IQ groups are due to variations in facial shapes observed within the groups (Fig. 2). The distributions of the variations in facial shapes are graphically presented in the results of 5 significant relative warps (Figures 3 and 4) generating general descriptions for comparison between and within male and female IQ groups. The differences between the groups are shown by the results of Kruskall-Wallis test (Table 5), a non-parametric multiple comparison test used to ascertain whether the intermediate scores are significantly different [11, 25]. Results show differences in some groups but these observations were not consistent for both sexes and in the sequence of the IQ scores. These were further substantiated when the relative warp scores was regressed with the scores of intelligence or IQ scores. The r value or the coefficient of determination and p value obtained to determine if there is correlation between the relative warp scores of coordinates and IQ scores show no relationship between facial morphospace and IQ (Figures 5 and 6, Table 6). No significant correlation between face shapes and IQ scores revealing that facial traits are not directly linked to high level of intelligence. Differences observed between IQ groups are due to the existence of variations in face shapes within them. The results of this study is similar to the results of the study conducted [15] where they have also found no correlation between morphological traits and real intelligence measured with IQ test, either in men or women. They have however argued that the faces of supposed high and low intelligence probably represent nothing more than a cultural stereotype because these morphological traits do not correlate with the real intelligence of the subjects.


Correlational analysis using bivariate linear regression revealed no linear interpolation between facial morphology using relative warp scores and measured intelligence between and within male and female individuals. Despite no correlation, the application of relative warp analysis from landmark-based geometric morphometries presented effectively detailed and specific variations as described between and within male and female population.


[1] Azoulay, K.G., 2006. Reflections on race and the biologization of difference. Patterns Prejudice, 40: 353-79.

[2] Plomin, R. and F.M. Spinath, 2004. Intelligence: Genetics, Genes, and Genomics. Journal of Personality and Social Psychology, 86(1): 112-129.

[3] Zebrowitz, L.A., 1997. Reading faces: Window to the soul? Boulder, CO: Westview Press.

[4] Todorov, A., S. Christopher and S.C. Verovsky, 2011. Personality impressions from facial appearance. In: Calder A, Rhodes G, Johnson M, Haxby J, editors. The Oxford handbook of face perception. Oxford: Oxford University Press.

[5] Kleisner, K., L. Priplatova, P. Frost and J. Flegr, 2013. Trustworthy-looking face meets brown eyes. Czech Republic: Charles University in Prague. PLoS One 8(1): e53285.

[6] Bruce, V. and A.W. Young, 1998. In the eye of the beholder: The science of face perception. Oxford: Oxford University Press.

[7] Valentova, J.V., K. Kelisner, J. Havh'c"ek and J. Neustupa, 2014. Shape Differences Between the Faces of Homosexual and Heterosexual Men. Czech Republic: Charles University in Prague. Arch Sex Behav 43: 353-361.

[8] Stephen, I.D., V. Coetzee and D.I. Perrett, 2011. Carotenoid and melanin pigment coloration affect perceived human health. Evolution and Human Behavior, 32: 216-227.

[9] Chinthapalli, K., E. Bartolini, J. Novy, M. Suttie, C. Marini, M. Falchi, Z. Fox, L.M.S. Clayton, J.W. Sander, R. Guerrini, C. Depondt, R. Hennekam, P. Hammond and S.M. Sisodiya, 2012. Atypical face shape and genomic structural variants in epilepsy. Brain, 135(10): 3101-14.

[10] Solon, C.C.E., M.A.J. Torres and C.G. Demayo, 2012. Describing the shape of the face of hypertensive and non-hypertensive adult females using geometric morphometric analysis. Human & Veterinary Medicine. International Journal of the Bioflux Society, 4(1): 45-51.

[11] Anies, O., M.A.J. Torres, M.M. Manting and C.G. Demayo, 2013. Landmark-Based Geometric Morphometrics in Describing Facial Shape of the Sama-Banguingui Tribe from the Philippines. Engineering and Technology Publishing. Journal of Medical and Bioengineering, 2(2): 131-136.

[12] Rule, N.O. and N. Ambady, 2010. Democrats and Republicans Can Be Differentiated from Their Faces. Plos One, 5(1): e8733.

[13] Burke, D. and D. Sulikowski, 2010. A new viewpoint on the evolution of sexually dimorphic human faces. Evol Psychol, 8(4): 573-85.

[14] Pflueger, L.S., Oberzaucher, S. Katina, I.J. Holzleitner and K. Grammer, 2012. Cues to fertility: perceived attractiveness and facial shape predict reproductive success. Evolution and Human Behavior 33:708-714.

[15] Kleisner K., V. Chvatalova and J. Flegr, 2014. Perceived Intelligence Is Associated with Measured Intelligence in Men but Not Women. Czech Republic: Charles University in Prague. PLoS ONE 9(3): e81237.

[16] Samochowiec, J., M. Wanke and K. Fiedler, 2010. Political Ideology at Face Value. Social Psychological and Personality Science, 1: 206-213.

[17] Kluckhohn, C. and H.A. Murray, 1948. Personality in nature, culture and society. New York: Knopf.

[18] Gottfredson, L. and D.H. Saklofske, 2009. Intelligence: Foundations and Issues in Assessment. Canadian Psychological Association, 50(3): 183-195.

[19] Borkenau, P. and A. Liebler, 1995. Observable attributes as manifestations and cues of personality and intelligence. Journal of Personality, 63(1): 1-25.

[20] Burrows, A.M., 2008. The facial expression musculature in primates and its evolutionary significance. Bioessays, 30(3): 212-225.

[21] Perez, S.I., V. Bernal and P.N. Gonzales, 2006. Differences between sliding semi-landmark methods in geometric morphometrics, with an application to human craniofacial and dental variation. Journal Anatomy, 208(6): 769-784.

[22] Smith, H.F., T. Ritzman, E. Otarola-Castillo and C.E. Terhune, 2013. A 3-D geometric morphometric study of intraspecific variation in the ontogeny of the temporal bone in modern Homo sapiens. Journal of Human Evolution, 65(5): 479-489.

[23] Rohlf, F.J. and L.F. Marcus, 1993. "A revolution in morphometries". Trends in Ecology and Evolution, 8: 129-132.

[24] Adams, D.C., F.J. Rohlf and D.E. Slice, 2004. Geometric morphometrics: ten years of progress following the revolution. Ital J Zool, 71: 5-16.

[25] Dapar, M.L.G., S.M.G. Garcia, M.V.D. Achacoso, C.A.P. Debalucos, C.S. Moneva and C.G. Demayo, 2014. Describing Populations of Pomacea canaliculata Lamarck from Selected Areas in Mindanao, Philippines using Relative warp analysis of the whorl shell shape. Australian Journal of Basic and Applied Sciences, 8(5): 355-360.

[26] Rohlf, F.J., 1990. Rotational fit (Procrustes) methods. Proc. Michigan Morphometrics Workshop, F.J. Rohlf and F.L. Bookstein Eds, Special publication, Museum of Zoology, Michigan: University of Michigan.

[27] Rohlf, F.J., 2000. "Statistical power comparisons among alternative morphometric methods," Am J Phys Anthropol, 111: 463-478.

[28] Baylac, M., C. Villemant and G. Simbolotti, 2003. Combining geometric morphometrics with pattern recognition for the investigation of species complexes. Biol J Linn Soc, 80: 89-98.

[29] Lynch, J.M., C.G. Wood and S.A. Luboga, 1996. Geometric morphometrics in primatology: craniofacial variation in homosapiens and pan troglodytes. Folia Primatol, 67(1): 15-39.

[30] Monteiro, L.R. and A.S. Abe, 1999. Functional and historical determinants of shape in the scapula of xenarthran mammals:evolution of a complex morphological structure. J Morph, 241: 251-263.

[31] Monteiro, L.R., J.A.F. Diniz-Filho, S.F. Reis and E.D. Araujo, 2002. Geometric estimates of heritability in biological shape. Evolution, 56(3): 563-572.

[32] Reis, S.F., L.C. Duarte, L.R. Monteiro and F.J. Von Zuben, 2002. Geographic variation in cranial morphology in thrichomys apereoides (Rodentia: Echimyidae). I. Geometric descriptors of shape and multivariate analysis of geographic variation in shape. J Mammal, 83(2): 333-344.

[33] Rohlf, F.J., 2004. tpsDig-Thin Plate Spline Digitizer Version 2.0. Department of Ecology and Evolution, State University of NewYork at Stony Brook, New York.

[34] Demayo, C.G., M.A.J. Torres, P.R. Olvis and N. Manlegro, 2010. Face Shape Differences in Selected Indigenous Peoples Groups in Mindanao. The Internet Journal of Biological Anthropology, 4(1): 1-22.

[35] Rohlf, F.J., 2008. tpsRelw (version 1.46). New York: Department of Ecology and Evolution, State University of New York at Stony Brook.

[36] Hammer, O., D.A.T. Harper and P.D. Ryan, 2001. PAST: Paleontological statistics software package for education and data. Analysis. Palaeontologia Electronica, 4: 1-9.

(1) Mark Lloyd G. Dapar, (1) Muhmin Michael E. Manting, (2) Luzvilla G. Sasan, (2) Rhoda Grace G. Arimao and (1) Cesar G. Demayo

(1) Department of Biological Sciences, MSU-Iligan Institute of Technology, Iligan City, Philippines

(2) Ouidance and Counselling Center, MSU-Iligan Institute of Technology, Iligan City, Philippines


Article history:

Received 23 June 2015

Accepted 25 July 2015

Available online 30 August 2015

Corresponding Author: Cesar G. Demayo, Department of Biological Sciences, College of Science and Mathematics, MSU-Iligan Institute of Technology, Iligan City, Philippines.


Table 1: The 43 anatomical landmarks of the face defined
in the study.

Landmark   Description of Landmark                          Type

1          Midpoint of the hairline                         II
2          The midpoint of the nasofrontal suture           II
3          The highest point on the upper margin of the     II
             middle portion of the eyebrow (left)
4          The highest point on the upper margin of the     II
             middle portion of the eyebrow (right)
5          The most lateral point of the eyebrow (left)     II
6          The most lateral point of the eyebrow (right)    II
7          The highest point of the eyelid (left)           II
8          The highest point of the eyelid (right)          II
9          Medial hinge of the eyelid (left)                I
10         Medial hinge of the eyelid (right)               I
11         Lateral hinge of the eyelid (left)               I
12         Lateral hinge of the eyelid (right)              I
13         Lowest point on the middle of the margin of      II
             the lower eyelid (left)
14         Lowest point on the middle of the margin of      II
             the lower eyelid (right)
15         The deepest point of the nasofrontal angle       II
16         Most protruded point of the nasal tip            II
17         Most lateral point on the nasal ala (left)       II
18         Most lateral point on the nasal ala (right)      II
19         Most lateral point of the nose (left)            I
20         Most lateral point of the nose (right)           I
21         Most inner point between the nose tip and        I
             the upper lip
22         Highest point of the upper lip (left)            I
23         Highest point of the upper lip (right)           I
24         The midpoint of the vermilion border of the      I
             upper lip
25         Most lateral point where the upper and lower     I
             lip meet (left)
26         Most lateral point where the upper and lower     I
             lip meet (right)
27         Midline point where the upper and lower          II
             lip meet
28         Midpoint of the lower margin of the lower lip    I
29         Most anterior point of the chin                  II
30         Lowest point in the midline on the lower         II
             border of the chin
31         Most lateral point at the angle of the           II
             mandible (left)
32         Most lateral point at the angle of the           II
             mandible (right)
33         The most lateral point on the zygomatic          II
             arch (left)
34         The most lateral point on the zygomatic          II
             arch (right)
35         Nose bridge                                      II
36         Medial point of the nasal ala outer              II
             margin (left)
37         Medial point of the nasal ala outer              II
             margin (right)
38         Lowest lateral point of the nasal ala            II
             inner margin (left)
39         Lowest lateral point of the nasal ala            II
             inner margin (right)
40         Highest point of the nasal ala inner             II
             margin (left)
41         Highest point of the nasal ala inner             II
             margin (right)
42         Medial point of the nasal ala inner              II
             margin (left)
43         Medial point of the nasal ala inner              II
             margin (right)

Type I landmark--joints of tissues or bones; Type II
landmark--maximal curvatures

Table 2: Total Distribution of Student

IQ Groups   Males   Females   Total

BA           14       23       37
AVE           8       10       18
AA           27       47       74
H             8        9       17
VH            8        9       17
S             3        4        7
Overall                        170

Table 3: Descriptions of shape variation for all females
exhibited by RW


RW                    (-)                      (+)

1 (29.55%)     Increased size of        Decreased size of
             forehead Thinner lips    forehead Thicker lips
                 Shortened chin           Elongated chin

2 (12.82%)     Shoter nasofrontal       Longer nasofrontal
              length Smaller face       length Larger face

3 (8.90%)       Narrow jaw, less        Wider jaw, rounded
                  rounded chin                 chin

4 (7.58%)         Larger nose              Smaller nose

5 (6.84%)      Wider nasolateral       Narrower nasolateral
                     margin                   margin


RW                    (-)                      (+)

1 (32.92%)     Increased size of        Decreased size of
              forehead Larger eyes    forehead Smaller eyes
             Thinner lips Shortened   Thicker lips Elongated
                      chin                     chin

2 (10.03%)    Uplifted corners of      Drooping corners of
             the mouth Larger nose    the mouth Smaller nose

3 (9.35%)          Wider jaw               Narrower jaw

4 (7.74%)     Elevated cheekbones       Lowered cheekbones

5 (5.78%)     Narrower nasolateral      Wider nasolateral
                     margin                   margin

Table 4: Canonical variates analysis of relative
warp scores.


Wilk's Lambda   df1    df2      F         P
0.7408          25    722.2   2.43    0.000133
Pillai Trace
0.2824          25     990    2.37    0.0001832


Wilk's Lambda   df1    df2      F         P
0.4409          30    1170    8.862   4.455E-35
Pillai Trace
0.6798          30    1480    7.762   1.775E-30

Table 5: Kruskall-Wallis test comparing the RW
scores of the six IQ groups.

IQ      AVE         BA         H          S          VH

AA    0.441592   0.016368   0.068945   0.001198   0.026117
AVE      --      0.219441   0.057743   0.059094   0.313096
BA                  --      0.004187   0.062527   0.882823
H                              --      0.017586   0.014782
S                                         --      0.210241
VH                                                   --


AA    2.32E-10   1.31E-19   2.30E-13   1.45E-10   1.96E-10
AVE      --      0.100639   0.000366   0.009587   0.087495
BA                  --      0.002326   0.000554   0.280323
H                              --      0.002564   0.000171
S                                         --      0.001565
VH                                                   --

BA-below average; AVE-average; AA-above average;
H-high; VH-very high; S-superior

* significant, p<0.05

Table 6: Correlation analysis of the relationship
between face shapes and IQ scores.


RW      slope          r        P(uncorr)

1    0.00033813     0.14042      0.45161
2    0.00012709     0.093638     0.18282
3    -5.8699E-05   -0.048678     0.48931
4    -2.8147E-05   -0.025694     0.71527
5    -0.00011423    -0.11329     0.10665


RW      slope          r        P(uncorr)

1    9.3197E-06    0.0048041     0.93363
2    -7.2115E-06    0.004817     0.93345
3    7.75717E-05    0.058641     0.30895
4    7.7188E-05     0.066305     0.24987
5    9.1272E-06    0.0083979     0.88426
6    -1.4227E-06   -0.0015563    0.97848
COPYRIGHT 2015 American-Eurasian Network for Scientific Information
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2015 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Dapar, Mark Lloyd G.; Manting, Muhmin Michael E.; Sasan, Luzvilla G.; Arimao, Rhoda Grace G.; Demayo
Publication:Advances in Environmental Biology
Article Type:Report
Geographic Code:9PHIL
Date:Aug 1, 2015
Previous Article:Sclerotial formation inhibition by vitamin A, C, and E in Aspergillus flavus.
Next Article:Phytochemical and antibacterial properties of the ethanolic leaf extract of Merremia peltata (L.) Merr. and Rubus spp.

Terms of use | Privacy policy | Copyright © 2020 Farlex, Inc. | Feedback | For webmasters