|Year : 2017 | Volume
| Issue : 1 | Page : 46-52
Relationship of orbital dimensions and facial angles with thumbprints ridge count among hausa ethnic group of Nigeria
Lawan Hassan Adamu1, Samuel Adeniyi Ojo2, Barnabas Danborno3, Samuel Sunday Adebisi3, Magaji Garba Taura4
1 Department of Anatomy, Faculty of Basic Medical Sciences, Bayero University, Kano, Kano State, Nigeria
2 Department of Human Anatomy, Faculty of Medicine, Ahmadu Bello University, Zaria, Kaduna State, Nigeria
3 Department of Veterinary Anatomy, Faculty of Veterinary Medicine, Ahmadu Bello University, Zaria, Nigeria
4 Department of Anatomy, Faculty of Basic Medical Sciences, Bayero University, Kano, Kano State, Nigeria; Department of Anatomy, College of Medicine, University of Bisha, Bisha, Saudi Arabia
|Date of Web Publication||9-Aug-2017|
Lawan Hassan Adamu
Department of Anatomy, Faculty of Basic Medical Sciences, Bayero University, PMB 3011, Kano, Kano State
Source of Support: None, Conflict of Interest: None
Background: Higher level of uniqueness exhibited by fingerprints and face across different individuals, sex, and population may suggest that there could be some mechanisms that control the two simultaneously.
Objectives: The objectives of the study were to determine the correlation between thumbprint ridge counts with facial distances and angles and to predict the facial distances and angles from thumbprints ridge counts among Hausa Ethnic Group.
Materials and Methods: This was a cross-sectional study. The study population comprises 457 participants. The ridged count was determined from ulnar, radial, and proximal areas of the thumbs. The facial distances and angles were measured from two-dimensional images. Pearson's correlation and stepwise multiple regression analyses were used for relationship and prediction, respectively.
Results: It was observed that in males, a significant negative correlation was observed between left orbital height and radial ridge counts. The right and left proximal ridge counts were found to correlate negatively with interocular distances and left orbital width, respectively. The nasion angle significantly correlated negatively with right ulnar ridge counts and proximal ridge counts, whereas the left proximal ridge count correlated positively with proximal ridge counts. In females, only the right and left ulnar ridge counts correlated negatively and positively with nasomental angle and right orbital width, respectively. Facial angles were predicted from ulnar ridge counts in both sexes. In females, the left ulnar ridge count predicts the right orbital height and width. The left and right orbital heights were best predicted by left proximal ridge count.
Conclusion: The thumbprint ridge count correlates with facial distances and angles. Different types of facial dimensions and angles can be predicted from thumbprint ridge counts.
Keywords: Facial angles, facial distances, Hausas population, ridge counts
|How to cite this article:|
Adamu LH, Ojo SA, Danborno B, Adebisi SS, Taura MG. Relationship of orbital dimensions and facial angles with thumbprints ridge count among hausa ethnic group of Nigeria. J Exp Clin Anat 2017;16:46-52
|How to cite this URL:|
Adamu LH, Ojo SA, Danborno B, Adebisi SS, Taura MG. Relationship of orbital dimensions and facial angles with thumbprints ridge count among hausa ethnic group of Nigeria. J Exp Clin Anat [serial online] 2017 [cited 2018 Sep 26];16:46-52. Available from: http://www.jecajournal.org/text.asp?2017/16/1/46/212641
| Introduction|| |
Determination of individual variation using fingerprint features has long been considered as a useful marker within the domain of biological anthropology (Reddy et al., 2000; Karmaka et al., 2006; Siváková et al., 2007; Karmakar et al., 2008). In the case of faces, the situation is very similar to fingerprints. For forensic purposes, relatively few studies have considered facial assessment although a promising result has been obtained, especially with regard to estimation of other parameters in the living (Ferrario et al., 2000; Ferrario et al., 2003), defining sexual dimorphism (Ferrario et al., 1995) and traits specific to ethnic groups (Le et al., 2002; Porter, 2004; Farkas et al., 2007; Roelofse et al., 2008). It has been generally accepted that facial traits may be extremely useful if used with caution for aging, sexing, determining ancestry, and even in the initial phases of personal identification within the forensic domain (Ritz-Timme et al., 2011). Therefore, prediction of facial parameters from other body variables, especially fingerprints, may be useful in ascertaining some information related to a particular suspect.
As it is well established that parts of the body may not always be available for analyses, it becomes necessary to make use of other parts of the body to predict other parts for personal identification (Krishan, 2008). Higher level of uniqueness exhibited by fingerprints and face across different individuals, sex, and population (Cummins and Midlo, 1961; Farkas et al., 2005) may suggest that there could be some mechanisms that control the two simultaneously. Consequently, the similarity in this important attribute supports the idea that there might be some relationships among fingerprints and faces. Determination of this kind of relationship may give room for human characterization and identification. This may also have potential in narrowing down the investigation process and thus provide useful clues to expert in the process of identification. The aim of the study was to determine the relationship between thumbprints ridge count with facial distances and facial angles under the following objectives: first, to determine the correlation between thumbprint ridge counts with orbital distances and facial angles; second, to predict the orbital distances and facial angles (frontal and lateral) from thumbprints ridge count from ulnar, radial, and proximal areas of the thumb.
| Materials and Methods|| |
The participants of this study were recruited from Hausa population of Kano State, Nigeria [Figure 1]. This was a cross-sectional study. The study population comprises 457 (343 males and 114 females) participants. Participants were considered to belong to Hausa Ethnic Group when the grandparents belong to Hausa Ethnic Group. The participants who were apparently healthy whose thumbs and faces were free from any deformity or pathological changes were included in the study. Only individuals within the age range of 18–25 years were considered, and this was to minimize the effect of age on thumbprint ridges and facial profile. Any individuals outside these inclusion criteria were excluded from the study. Ethical approval was obtained from the Ethical Committee of Ahmadu Bello University, Teaching Hospital, Zaria, Faculty of Medicine (ABUTHZ/HREC/506/2015), and Kano State Hospitals Management Board. All the participants signed the consent form before the commencement of data collection.
|Figure 1: Map of Hausa States of Nigeria showing the location of the study area (Kano)|
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Determination of ridge density
A plain thumbprint was captured using a fingerprint scanner (digital persona, China). The thumbprints were classified into arches, whorls, and loops [Figure 2] (Cummins and Midlo, 1943). Acree (1999) and Gutiérrez-Redomero et al. (2008) methods were used to determine the ridged counts in the three areas (ulnar, radial, and proximal) of the thumbs [Figure 2].
|Figure 2: Spaces (5 mm × 5 mm) on ulnar, radial, and proximal of fingerprint for ridge density determination for three classes of fingerprints|
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Measurement of facial dimensions
The face was captured using a digital camera (Samsung, ES90, 4.9-25.5mm HD, China) placed on a tripod stand (WT3570, China) to standardize the distance (100 cm) between the individual and the camera. The tripod was used to avoid undesirable movements of operator and camera while taking the photographs (Morosini et al., 2012). The frontal and lateral photographs were obtained using the method described by Moorrees (1994) with individual's head in Broca's Natural Head Position (Ferrario et al., 1993). The captured images were saved to a personal computer in jpeg format for processing and analyses.
Facial landmarks, linear dimensions, and angles
Seven anatomical landmarks [Table 1] were recognized for the measurement of facial dimensions and angles (Farkas, 1994; Porter and Olson, 2001; Oghenemavwe et al., 2010; Gibellia et al., 2012). Three paired facial dimensions measured include interocular distance (en-en), orbital height (ps-pi), and orbital width (ex-en). The facial linear dimensions were obtained as the distance between one anatomical landmark to another. From the lateral photographs, four lateral angles nasofrontal angle, mentocervical angle, nasofacial angle, and nasomental angle (Oghenemavwe et al., 2010; Reddy et al., 2012) and two frontal facial angles nasion angle and facial aperture modified angle were measured (Morosini et al., 2012) [Figure 3].
Statistical analysis and measurement error
Descriptive statistic of mean ± standard deviation was used to express the data; Pearson's correlation analysis was used for the relationship between thumbprint ridge density and facial linear dimensions. Stepwise multiple regression analysis was used to predict facial distances from thumbprint ridge density. SPSS version 20 statistical software (IBM Corporation, NY, USA) was used for the statistical analysis, and P < 0.05 was set as level of significance.
Two sets of measurements (from thirty randomly selected participants) were taken and compared using technical error of measurement (TEM) to determine the precision of measurement (Aldridge et al., 2005). Absolute TEM = √Σdi 2/2n, where: Σd 2 = summation of deviations (the difference between the 1st and 2nd measurements) raised to the second power; n = number of volunteers measured; i = the number of deviations. The absolute TEM was expressed as percentages as follows: relative , where VAV = variable average value; this is the arithmetic mean of the mean between both measurements obtained ( first and second measurements) of each volunteer for the same variable. The percentage scores exceeding 10% were deemed “poor” (Perini et al., 2005; Weinberg et al., 2004). The intraclass correlation (ICC) was used to demonstrate the strength of the relationship (similarities) between first and second measurements. The values for the reliability coefficient (r) ranged from 0 to 1, where ICC <0 indicated “no reliability” and 0.6–<0.8 “substantial reliability.” (Shrout and Fleiss, 1979). The entire variables in this study are within the acceptable measurement error.
| Results|| |
The results showed that significant sexual dimorphism in mean ridge count occurred only in the right ulnar ridge count and left proximal ridge count. Comparison between the three areas of the thumb showed lower ridge count in the proximal ridge counts in both sides and sexes. The highest count in males was observed in radial ridge count, whereas in females, the ulnar ridge count was higher [Table 2]. In linear facial dimensions, only orbital width exhibits a significant sexual dimorphism with females having higher mean value. For facial angles, only facial aperture modified and nasion angles exhibited no sex differences. In all the angles that exhibit sex difference, males tend to have higher mean values except for nasofacial angles [Table 3].
|Table 2: Mean and standard deviation of ridge count of males and females thumbs|
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|Table 3: The mean and standard deviation of facial dimensions and anglesof males and females|
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In males, a significant negative correlation was observed between left orbital height and radial ridge counts. The right and left proximal ridge counts were found to correlate negatively with interocular distances and left orbital width, respectively. The nasion angle significantly correlated negatively with right ulnar ridge counts and proximal ridge counts, whereas the left proximal ridge count as the thumbprints variables correlated positively with mentocervical angle [Table 4]. In females, only the right and left ulnar ridge counts correlated negatively and positively with nasomental angle and right orbital width, respectively [Table 5].
|Table 4: Correlation between thumbprint count with facial dimensions and angles in males|
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|Table 5: Correlation between thumbprint count with facialdimensions and angles in females|
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From [Table 6], facial angle was predicted from ulnar ridge counts in both sexes. In females, the left ulnar ridge count predicts the right orbital height and width. For males, the right radial ridge count and proximal ridge counts contribute to the prediction of the left orbital height and interocular distance, respectively. The left and right orbital heights were best predicted by left proximal ridge count.
|Table 6: Stepwise multiple regression analysis for prediction of facial variables from thumbprint ridge count in male and female|
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| Discussion|| |
Biometrics can simply be interpreted as identification of an individual based on his/her physical, physiological, or behavioral characteristics (Papoulis, 1965). It must, therefore, absolutely have the capability to differentiate between a genuine individual and a fraudulent impostor (Papoulis, 1965; Newham, 1995). Universality, uniqueness, permanence, and collectability are the requirements for a character to be used as a biometric indicator to make a personal identity (Krishan et al., 2010). The aim of the study was to determine the relationship between thumbprints ridge count with facial distances and facial angles.
A significant negative correlation observed in both facial distances and angles with ridge count had inverse relationship between the variables. This indicates that individuals with higher ridge counts will have low facial distances and angles. This can be explained by the fact that both tip of the finger and face can respond equally to the proportional increase in the body size of an individual as the age advances. It was reported that in heavier and taller individuals, the fingerprints ridges tend to be coarse compared to in the smaller body frame (Krishan et al., 2010). This implies that the same numbers of ridges are accommodated among the individuals with less body build in a small surface area; thus, a higher density is noted among the individuals with less body build (Krishan et al., 2010; Eshak et al., 2013). It was noted that for every increase in body stature, there are corresponding decreases in some facial characters and also increases in facial characters (Zhuang et al., 2010). This explained the present finding where negative correlation indicated increases in ridge count with corresponding decrease in facial distances.
Furthermore, as body height increases facial height also increases, as such facial height is good estimator of height (Krishan, 2008). This may explain the negative relationship between the ridge count and facial distances. However, with respect to angles, a positive correlation indicates that proportionality exists between the variables. This can be appreciated due to the fact that the facial angles normally may depend on the prominence of the facial landmarks, for example, height decreases with increase in facial width (Ozkaya and Sagiroglu, 2010). Hence, right orbital width correlates positively with ridge counts, and the ridge count on the other hand decreases with increase in body proportion. From this explanation, it may be suggested that the face parameters and fingerprints variables may be predicted from one another.
In the present study, the different parts of the thumbprints have different potentials in the prediction of the facial variables. This finding may be supported by the fact that identical twins possess strong similarity in fingerprints in addition to faces (Jain et al., 2002). Increasing and decreasing distinctions of such similarities are also the same among nonrelated individuals (Ozkaya and Sagiroglu, 2010). It was also proven experimentally by generating several components of the facial trait from fingerprints. These include the face models of eyebrows, eyes, and mouth (Ozkaya and Sagiroglu, 2008); inner face parts including eyes, nose, and mouth (Ozkaya and Sagiroglu, 2008); face borders; and ears (Ozkaya and Sagiroglu, 2008). However, the specific feature of the fingerprints that determine the specific facial features was not demonstrated. In another study, some level of relationship was also observed between the thumbprints patterns and facial proportions (Adamu et al., 2017). This supports the present study and suggested the existence of possible mechanisms that may control the face and fingerprints during developments. It was also reported that the fingerprints and face variables share some level of genetic mechanism that control their developments (Claes et al., 2014; Ho et al., 2016).
In the field of biological anthropology, there is a need of population-specific formulae for prediction of one variable from another. This study provides models for the prediction of facial distances and angles among Hausa population of Nigeria. The application of the present study in the field of forensic sciences and law enforcement agents is very clear. This is due to the fact that prediction of facial variables from thumbprint variables (ridges) may also further narrow down the investigation process by forensic experts. In this context, the relationship between the other fingerprint variables with other facial variables needs to be established in trying to provide comprehensive relationship between the two variables. Moreover, the relationship also needs to be established in other populations due to the need of comparison and valid conclusion on this relationship.
| Conclusion|| |
The thumbprint ridge count correlates with facial distances and angles in the Hausa population of Nigeria. Different types of facial dimensions and angles can be predicted from thumbprint ridge counts. The face and fingerprint may be controlled by the same intrinsic factor during embryonic period.
Declaration of patient consent
The authors certify that they have obtained all appropriate patient consent forms. In the form the patient(s) has/have given his/her/their consent for his/her/their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.
This work is an extract of a Ph.D. dissertation which was sponsored by Bayero University Research Grant Unit and Tertiary Education Trust Fund (TET fund) of Nigeria.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Acree M.A. (1999). Is there a gender difference in fingerprint ridge density? Forensic Sci Int 102 (1):35-44.
Adamu L.H., Ojo S.A., Danborno B., Adebisi S.S., Taura M.G. (2017). Evaluation of facial proportions and its association with thumbprint patterns among Hausa ethnic group. J Anthropol 1-11. [Doi: 10.1155/2017/9571470].
Aldridge K., Boyadjiev S.A., Capone G.T., DeLeon V.B., Richtsmeier JT. (2005). Precision and error of three dimensional phenotypic measures acquired from 3dMD photogrammetric images. Am J Med Genet 138A (3):247-53
Claes P., Liberton D.K., Daniels K., Rosana K.M., Quillen E.E., Pearson L.N., et al.
(2014). Modeling 3D facial shape from DNA. PLoS Genet 10 (3):e1004224.
Cummins H., Midlo C. (1943). Finger Prints, Palms and Soles. Blakiston, Philadelphia.
Cummins H., Midlo C. (1961). Fingerprints, Palms and Soles: An Introduction to Dermatoglyphics. Dover Publications Inc., New York, USA.
Eshak G.A., Zaher J.F., Hasan E.I., Ewis A.A. (2013). Sex identification from fingertip features in Egyptian population. J Forensic Leg Med 20 (1):46-50.
Farkas L.G. (1994). Examination. In: Farkas L.G., editor. Anthropometry of the Head and Face. 2nd
ed. Raven Press, New York, p. 3-56.
Farkas L.G., Katic M.J., Forrest C.R., Alt K.W., Bagic I., Baltadjiev G., et al
. (2005). International anthropometric study of facial morphology in various ethnic groups/races. J Craniofac Surg 16 (4):615-46.
Farkas L.G., Katic M.J., Forrest CR. (2007). Comparison of craniofacial measurements of young adult African-American and North American White males and females. Ann Plast Surg 59 (6):692-775.
Ferrario V.F., Sforza C., Mianim A., Tartaglia G. (1993). Craniofacial morphometry by photografic evaluations. Am J Orthod Dentofacial Orthop 103 (4):327-37.
Ferrario V.F., Sforza C., Schmitz J.H., Ciusa V., Colombo A. (2000). Normal growth and development of the lips: A 3-dimensional study from 6 years to adulthood using a geometric model. J Anat 196:415-23.
Ferrario V.F., Sforza C., Serrao G., Ciusa V., Dellavia C. (2003). Growth and aging of facial soft tissues: A computerized three-dimensional mesh diagram analysis. Clin Anat 16 (5):420-33.
Ferrario V.F., Sforza C., Serrao G., Miani A Jr. (1995). A computerized non-invasive method for the assessment of human facial volume. J Craniomaxillofac Surg 23 (5):280-6.
Gibellia D., Mapellib A., Obertovàc Z., Poppa P., Gabrielc P., Ratnayake M., et al.
(2012). Age changes of facial measurements in European young adult males: Implications for the identification of the living. Homo 63 (6):451-8.
Gutiérrez-Redomero E., Alonso C., Romero E., Galera V. (2008). Variability of fingerprint ridge density in a sample of Spanish Caucasians and its application to sex determination. Forensic Sci Int 180 (1):17-22.
Ho Y.Y., Evans D.M., Montgomery G.W., Henders A.K., Kemp J.P., Timpson N.J., et al
. (2016). Common genetic variants influence whorls in fingerprint patterns. J Invest Dermatol 136 (4):859e862.
Jain A., Pankanti K., Prabhakar S., Hong S., Ross L., Wayman A., et al
. (2004). A grand challenge. In: Proceedings of the International Conference on Pattern Recognition. Vol. 2. IEEE Computer Society, Cambridge, UK, p. 935-42.
Jain A., Prabhakar S., Pankanti S. (2002). On the similarity of identical twin fingerprints. Pattern Recognit 35:2653-63.
Karmaka B., Yakovenko K., Kobyliansky E. (2006). Mode of inheritance of dermatoglyphic pattern intensity index on fingers in five Indian populations: A comparative study between individual trait and its factor. Am J Hum Biol 18 (3):377-86.
Karmakar B., Yakovenko K., Kobyliansky E. (2008). Sexual dimorphism in the Chuvashian population of Russia in two types of dermatoglyphic traits: Principal component analysis. Coll Anthropol 32 (2):467-77.
Krishan K. (2008). Estimation of stature from cephalo-facial anthropometry in North Indian population. Forensic Sci Int 181 (1-3):52.e1-6.
Krishan K., Ghosh A., Kanchan T., Ngangom C., Sen J. (2010). Sex differences in fingerprint ridge density – Causes and further observations. J Forensic Leg Med 17 (3):172–3.
Le T.T., Farkas L.G., Ngim R.C., Levin L.S., Forrest C.R. (2002). Proportionality in Asian and North American Caucasian faces using neoclassical facial canons as criteria. Aesthetic Plast Surg 26 (1):64-9.
Moorrees C.F. (1994). Natural head position – A revival. Am J Orthod Dentofacial Orthop 105 (5):512-3.
Morosini I.A., Peron A.P., Correia K.R., Moresca R. (2012). Study of face pleasantness using facial analysis in standardized frontal photographs. Dent Press J Orthod 17 (5):24-3.
Newham E. (1995). The Biometric Report. SJB Services, New York.
Oghenemavwe E.L., Osunwoke A.E., Ordu S.K., Omovigho O. (2010). Photometric analysis of soft tissue facial profile of adult Urhobos. Asian J Med Sci 2 (6):248-52.
Ozkaya N., Sagiroglu S. (2008). Face recognition from fingerprints. J Fac Eng Arch Gazi 23 (4):785-94.
Ozkaya N., Sagiroglu S. (2008). Intelligent Face Border Generation System from Fingerprints. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2008) in IEEE World Congress on Computational Intelligence (WCCI 2008), IEEE Catalog Number: CFP08FUZ-CDR, Congress: 2007907698, 1-6 June, Hong Kong.
Ozkaya N., Sagiroglu S. (2008). Intelligent Face Mask Prediction System, International Joint Conference on Neural Networks (IJCNN 2008) in IEEE World Congress on Computational Intelligence (WCCI 2008), IEEE Catalog Number: CFP08IJS-CDR, Library of Congress: 2007907698, 1-6 June, Hong Kong.
Ozkaya N., Sagiroglu S. (2010). Generating one biometric feature from another: Faces from fingerprints. Sensors (Basel) 10 (5):4206-37.
Papoulis A. (1965). Probability, Random Variables, and Stochastic Processes. McGraw-Hill, New York.
Perini T.A., Oliveira1 G.L., Ornellas J.S., de Oliveira F.P. (2005). Technical error of measurement in anthropometry. Rev Bras Med Sport 11 (1):86-90.
Porter J.P. (2004). The average African American male face: An anthropometric analysis. Arch Facial Plast Surg 6 (2):78-81.
Porter J.P., Olson K.L. (2001). Anthropometric facial analysis of the African American woman. Arch Facial Plast Surg 3 (3):191-7.
Reddy B.M., Chopra V.P., Karmakar B., Malhotra K.C., Mueller H. (2000). Quantitative dermatoglyphics and population structure in Northwest India. Am J Hum Biol 12 (3):315-26.
Reddy M., Aliyu N.K., Raghav P., Kundu V., Mishra V. (2012). A computer assisted angular photogrammetric analysis of the soft time facial profile of North Indian adults. J Indian Orthod Soc 45 (3):119-23.
Ritz-Timme S., Gabriel P., Tutkuviene J., Poppa P., Obertova Z., Gibelli D., et al.
(2011). Metric and morphological assessment of facial features: A study on three European populations. Forensic Sci Int 207 (1-3):239.e1-8.
Roelofse M.M., Steyn M., Becker P.J. (2008). Photo identification: Facial metric and morphological features in South African males. Forensic Sci Int 177:168-75.
Shrout P., Fleiss J. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychol Bull 86 (2):420-8.
Siváková D., Scheil H.G., Schmidt H.D., Vulpe C. (2007). Population affinities assessed by dermatoglyphic and hemogenetic variables. Anthropol Anz 65 (2):137-46.
Weinberg S.M., Scott N.M., Neiswanger K., Brandon C.A., Marazita M.L. (2004). Digital three-dimensional photogrammetry: Evaluation of anthropometric precision and accuracy using a Gene×3D camera system. Cleft Palate Craniofac J 41 (5):507-18.
Zhuang Z., Landsitte D., Benson S., Roberge R., Shaffer R. (2010). Facial anthropometric differences among gender, ethnicity, and age groups. Ann Occup Hyg 54 (4):391-402.
[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]