|Year : 2017 | Volume
| Issue : 4 | Page : 223-228
Prediction of facial height, width, and ratio from thumbprints ridge count and its possible applications
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, Kaduna State, 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, Saudi Arabia
|Date of Web Publication||11-Jan-2018|
Dr. Lawan Hassan Adamu
Department of Anatomy, Faculty of Basic Medical Sciences,
Bayero University Kano, PMB 3011, Kano, Kano State
Source of Support: None, Conflict of Interest: None
The fingerprints and face recognition are two biometric processes that comprise methods for uniquely recognizing humans based on certain number of intrinsic physical or behavioral traits. The objectives of the study were to predict the facial height (FH), facial width, and ratios from thumbprints ridge count and its possible applications. This was a cross-sectional study. A total of 457 participants were recruited. A fingerprint live scanner was used to capture the plain thumbprint. The facial photograph was captured using a digital camera. Pearson's correlation analysis was used for the relationship between thumbprint ridge density and facial linear dimensions. Step-wise linear multiple regression analysis was used to predict facial distances from thumbprint ridge density. The result showed that in males the right ulnar ridge count correlates negatively with lower facial width (LFW), upper facial width/upper FH (UFW/UFH), lower FH/FH (LFH/FH), and positively with UFH and UFW/LFW. The right and left proximal ridge counts correlate with LFW and UFH, respectively. In males, the right ulnar ridge count predicts LFW, UFW/LFW, UFW/UFH, and LFH/FH. Special upper face height I, LFW, height of lower third of the face, UFW/LFW was predicted by right radial ridge counts. LFH, height of lower third of the face, and LFH/FH were predicted from left ulnar ridge count whereas left proximal ridge count predicted LFW. In females only, the special upper face height I was predicted by right ulnar ridge count. In conclusion, thumbprint ridge counts can be used to predict FH, width, ratios among Hausa population. The possible application of fingerprints in facial characterization for used in human biology, paleodemography, and forensic science was demonstrated.
Keywords: Facial dimensions, facial ratios, Hausa ethnic group, ridge count
|How to cite this article:|
Adamu LH, Ojo SA, Danborno B, Adebisi SS, Taura MG. Prediction of facial height, width, and ratio from thumbprints ridge count and its possible applications. J Forensic Sci Med 2017;3:223-8
|How to cite this URL:|
Adamu LH, Ojo SA, Danborno B, Adebisi SS, Taura MG. Prediction of facial height, width, and ratio from thumbprints ridge count and its possible applications. J Forensic Sci Med [serial online] 2017 [cited 2019 Oct 17];3:223-8. Available from: http://www.jfsmonline.com/text.asp?2017/3/4/223/222894
| Introduction|| |
Human fingers are known to display friction ridge skin that consists of a series of ridges and furrows, generally referred to as fingerprints. Fingerprint friction ridge details are described in a hierarchical order (based on resolutions) ranging from level zero to three, in which the count will be possible only at level one. The facial trait of an individual has to be defined as the soft tissues of the face together with the underlying bony skeleton. The traits are the main indicative feature in physical appearance, which is always associated with social acceptance, psychological well-being, and self-esteem of an individual. The fingerprints and face recognition are two biometric processes that comprise methods for uniquely recognizing humans based on certain number of intrinsic physical or behavioral traits.
Previous studies correlate fingerprint variables with hand length and distal phalanx breadth. The relationship of epidermal ridges with certain body dimensions such as weight, height, and hand size and width, gripping and tactile sensitivity was also established.,,,, Using facial distances studies were also able to predict body height. Moreover, other parts of the body such as hands, trunk, intact vertebral column, upper and lower limbs, individual long and short bones, foot and footprints were also used to predict stature,,, It can be suggested that both the fingerprints and facial variables were used to estimate body parameters like height, Which was found to be correlated with several body variables not limited to hand parameters, short bones, long bones.
It is known fact that parts of the body may not always be available to inspect, which necessitates the use of other parts of the body such as frictional ridges to predict other parts such as facial distances. In addition, accurate facial features prediction such as facial height (FH), facial width, and facial ratio is essential for diagnosis of genetic and acquired anomalies for the study of normal and abnormal growth and for morphometric investigations.,
Despite the importance of fingerprints and face in establishing identity and usefulness of prediction of facial variables from other body variables like fingerprint ridges, there is paucity of data in the relationship between fingerprints ridge counts and facial variables among the Hausa ethnic group of Kano state, Nigeria. Therefore, the present study aimed at establishing this relationship through investigating the correlation that may exist between the thumbprint ridge counts with FH, facial width, and ratios. The study also investigated the potential of thumbprint ridge counts in prediction of FH, facial width, and ratios.
| Materials and Methods|| |
The study population comprises of 457 (343 males and 114 females) participants of Hausa ethnic group (at the level of maternal and paternal grandfather) of Kano State [Figure 1]. This was cross-sectional study. Apparently, healthy controls whose thumbs and face were free from any deformity or pathological changes were included. Only individuals within the age range of 18-25 years were considered in order to minimize the effect of age on thumbprint and facial parameters. Ethical approval was sought from ethical committee of Ahmadu Bello University, Teaching Hospital, Zaria, Faculty of Medicine (ABUTHZ/HREC/506/2015) and Kano State Hospitals Management Board. Informed consent was obtained from the participants.
Determination of ridge density
Each participant was asked to clean the tip of his/her thumb to remove any dirt associated with it. A fingerprint live scanner (digital persona, China) was used to capture the plain thumbprint. Each thumbprint was classified into one of the three patterns; loops, whorls, and arches as proposed by Cummins and Midlo. The ridge count for the ulnar, radial, and proximal areas was determined with the 25 mm2 as described by Acree, and Gutiérrez-Redomero et al. [Figure 2].
|Figure 2: Spaces (5 mm × 5 mm) on ulnar, radial, and proximal parts of finger for ridge count for three classes of fingerprint|
Click here to view
The facial photograph was captured using digital camera (Samsung, ES90, 4.9-25.5 mm HD, China) placed on a tripod stand (WT3570, China) to standardize the distance (100 cm) between the subject and camera. The tripod helps to prevent undesirable movements of operator and camera while taking photographs. The frontal photograph was obtained using the method described by Moorrees, with participant's head in Broca's Natural Head Position. The captured images were saved to personal computer in jpeg format for processing and analyses.
Facial landmarks, measurements, and ratios
Seven anatomical landmarks [Table 1] were recognized for measurement of six facial distances and four calculated facial ratios,, [Table 2].
|Table 2: Linear facial dimensions and ratios with their corresponding landmarks|
Click here to view
Two sets of measurements (taken from 30 randomly selected participants) were compared using technical error of measurement (TEM) to determine the precision of measurement. Absolute, where: = summation of deviations (the difference between the 1st and 2nd measurements) rose to the second power; n = number of volunteers measured, i = the number of deviations. The absolute TEM was expressed as percentages as follows; Relative TEM = , Where, VAV = Variable average value, this is the arithmetic mean of the mean between both measurements obtained (1st and 2nd measurements) of each volunteer for the same variable. The percentage scores exceeding 10% were deemed poor., 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,” 0.6-<0.8 “substantial reliability.” The entire variables in this study are within the acceptable measurement error.
Descriptive statistics of mean ± standard deviation was used to express the data; Pearson's correlation analysis was used for relationship between thumbprint ridge density and facial linear dimensions. Step-wise multiple regression analysis was used to predict facial distances from thumbprint ridge density. SPSS version 20 statistical software (IBM Corporation, Armonk, NY) was used for the statistical analysis and P < 0.05 was set as level of statistical significance.
| Results|| |
Lower ridge count was observed in the proximal ridge counts compared to the ulnar and radial areas in both sides and sexes. Males had higher mean count in radial ridge count whereas females had higher counts in ulnar ridge count. A significant sex variation in mean ridge count was observed only in right ulnar ridge count and left proximal ridge count [Table 3]. The entire facial distances showed significant differences except upper facial width (UFW). In all the variables, males had higher mean value compared to females except for upper FH (UFH) [Table 4].
|Table 3: The mean and standard deviation, ridge count of males and females of both left and right thumbs|
Click here to view
[Table 5] shows, in males, the right ulnar ridge count correlates negatively with lower facial width (LFW), UFW/UFH, lower FH (LFH)/FH, and positively with UFH and UFW/LFW. Similarly, left ulnar ridge counts correlate negatively with LFH, height of one-third of the face and LFH/FH. Special upper face height I and height of one-third of the face correlate negatively with right radial ridge counts. The right and left proximal ridge counts correlate with LFW and UFH, respectively. For females, no significant correlation was observed between the thumbprints ridge counts with facial distances and ratio [Table 6].
|Table 5: Correlation between right ridge density with facial dimensions and ratios in males|
Click here to view
|Table 6: Correlation between right ridge density with facial dimensions and ratios in females|
Click here to view
In males, the right ulnar ridge count predicts LFW, UFW/LFW, UFW/UFH, and LFH/FH. Special upper face height I, LFW, height of lower third of the face, UFW/LFW was predicted by right radial ridge counts. LFH, height of lower third of the face, and LFH/FH were predicted from left ulnar ridge count whereas left proximal ridge count predicted LFW. In females only, the special upper face height I was predicted by right ulnar ridge count [Table 7].
|Table 7: Stepwise multiple regression analysis for prediction of facial variables from thumbprint ridge count in males and females|
Click here to view
| Discussion|| |
Determination of individual variations using facial features and dermatoglyphics has long been considered as a useful marker within the domain of human biology and forensic science.,,,, Prediction of one variable from another may be considered as one of the criteria that may aid personal identification. This may have potential in narrowing down the investigation process, and thus provides useful clues to experts. In addition, this can provide useful concepts in human characterization. The main objective of this study was to predict FH, width, ratios using thumbprint ridge counts among Hausa population.
The correlation observed in the present study can be supported by a study that reported some level of relationship between the thumbprint pattern and facial proportion. Since the present study involved quantitative variables, the degree of relationship was quantified. It was obvious that some thumbprint ridge counts correlate negatively while others positively with facial variables. To explain this scenario, it was taking into account that some facial features responses to increase in body size. It was also reported that one of the reasons why males tend to have lower ridge count compared to females is due to sexual dimorphism in the body form, in which male on the average have higher stature., It was reported that for every 100 mm increase in body height, there is significant increase in some facial dimensions such as face width, nasal root breadth, and nose breadth as well as significant decrease in other variables such as bigonial breadth. This indicates that ridge count increases with increase in some facial distances (positive correlation) and similarly decreases with increase in some facial distances (negative correlation) as observed in the present study.
It is proven experimentally that the existence of the relationship among the biometric features of faces and fingerprints. This led to generating several components of facial traits from fingerprints. This includes face contours such as face border and ears, inner face parts, eyes, nose, and mouth, and the face parts such as eyebrows, eyes, and nose. In the present study, a population-specific formulae were generated to predict facial features from fingerprints ridge counts. The ridge count in all the three areas showed potentials in the prediction of the selected facial features. However, the ulnar ridge count best predicts most of the facial features in both sexes. This may be due to the fact that each area of the finger responds differently from developmental instruction. In addition, the ridge formation that determines the fingerprint pattern started independently in the different area of the thumb. Therefore, the areas may predict different body variables as observed in the present study.
In forensic context, prediction of facial variables from thumbprints may help to narrow the pool of potential suspects in the process of establishment of identity. Using partial fingerprints capture from criminal scene the facial profile of the suspect can be predicted. In addition, fingerprints are mostly deposited unintentionally during shaping, molding, and touching wet clay. Once dried and fired, the clay hardens and becomes chemically stable which allows the prints to be preserved indefinitely. Epidermal ridges and their arrangement exhibit a number of properties that reflect the biology of an individual. Based on this the present study can be used to determine the facial characteristic of an ancient population, hence, useful to paleoanthropologists and archaeologists. In other perspectives, this study may be used to highlight indirectly the health status of an ancient population; this is due to the fact that the facial dimensions and ratios have been shown to correlate with weight and body mass index. It was reported that facial features were significantly related to the body mass index., Therefore, by predicting the facial features from ancient fingerprint, a characteristic body mass index may be established for such population. This also may be of help where the body mass index is important to forensic examiner.
| Conclusion|| |
Relationship between the thumbprint ridge count with facial distances and ratio was established in the Hausa ethnic group of Nigeria. Thumbprint ridge counts can be used to predict FH, width, ratios among Hausa population. The possible application of fingerprints in facial characterization for used in forensic science, human biology, and paleodemography were demonstrated.
Financial support and sponsorship
This work is an extract of a Ph.D. dissertation which was sponsored by Bayero University Research Grant Unit and Tertiary Education Trust Fund (TETfund) of Nigeria.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Cummins H, Midlo C. Fingerprints, Palms and Soles: An Introduction to Dermatoglyphics. New York, USA: Dover Publications Inc.; 1961.
Jain AK, Chen Y, Demirkus M. Pores and ridges: High-resolution fingerprint matching using level 3 features. IEEE Trans Pattern Anal Mach Intell 2007;29:15-27.
Hershon LE, Giddon DB. Determinants of facial profile self-perception. Am J Orthod 1980;78:279-95.
Bolle RM, Connell JH, Pankanti S, Ratha NK, Senior AW. Guide to Biometrics. New York, USA: Springer Science + Business Media, Inc.; 2004. p. 3-6, 31-45, 146-148.
Cummins H, Waits WJ, McQuitty JT. The breadths of epidermal ridges on the finger tips and palms: A study of variation. Am J Anat 1941;68:127-50.
Ohler EA, Cummins H. Sexual differences in breadths of epidermal ridges on the finger tips and palms. Am J Phys Anthropol 1942;29:341-62.
Cummins H, Midlo C. Finger Prints, Palms and Soles. Philadelphia: The Blakiston Co.,:1943.
Loesch DZ, Martin NG. Finger ridge patterns and tactile sensitivity. Ann Hum Biol 1984;11:113-24.
Moore RT. Analysis of ridge-to-ridge distance on fingerprints. J Forensic Ident 1989;39:231-8.
Miller G. Neuroscience. Fingerprints enhance the sense of touch. Science 2009;323:572-3.
Krishan K. Estimation of stature from cephalo-facial anthropometry in north Indian population. Forensic Sci Int 2008;181:52.e1-6.
Pelin C, Duyar I, Kayahan EM, Zaǧyapan R, Aǧildere AM, Erar A, et al.
Body height estimation based on dimensions of sacral and coccygeal vertebrae. J Forensic Sci 2005;50:294-7.
Bidmos M. Adult stature reconstruction from the calcaneus of South Africans of European descent. J Clin Forensic Med 2006;13:247-52.
Duyar I, Pelin C, Zagyapan R. A new method of stature estimation for forensic anthropological application. Anthropol Sci 2006;114:23-7.
Kurnia C, Susiana S, Husin W. Facial indices in Chinese ethnic students aged 20-22. J Dent Indonesia 2012;19:1-4.
Yesmin T, Thwin SS, Urmi SA, Wai M, Fazlin Zaini P, Azwan K. Study of facial index among Malay population. J Anthropol 2014;2014:4.
Acree MA. Is there a gender difference in fingerprint ridge density? Forensic Sci Int 1999;102:35-44.
Gutiérrez-Redomero E, Alonso C, Romero E, Galera V. Variability of fingerprint ridge density in a sample of Spanish Caucasians and its application to sex determination. Forensic Sci Int 2008;180:17-22.
Morosini IA, Peron AP, Correia KR, Moresca R. Study of face pleasantness using facial analysis in standardized frontal photographs. Dent Press J Orthod 2012;17:24-3.
Moorrees CF. Natural head position - A revival. Am J Orthod Dentofacial Orthop 1994;105:512-3.
Ferrario VF, Sforza C, Mianim A, Tartaglia G. Craniofacial morphometry by photografic evaluations. Am J Orthod Dentofacial Orthop 1993;103:327-37.
Farkas LG, editor. Examination. In: Anthropometry of the Head and Face. 2nd
ed. New York: Raven Press; 1994. p. 3-56.
Porter JP, Olson KL. Anthropometric facial analysis of the African American woman. Arch Facial Plast Surg 2001;3:191-7.
Gibelli D, Mapelli A, Obertovà Z, Poppa P, Gabriel P, Ratnayake M, et al.
Age changes of facial measurements in European young adult males: Implications for the identification of the living. Homo 2012;63:451-8.
Aldridge K, Boyadjiev SA, Capone GT, DeLeon VB, Richtsmeier JT. Precision and error of three-dimensional phenotypic measures acquired from 3dMD photogrammetric images. Am J Med Genet A 2005;138A: 247-53.
Perini TA, Oliveira1 GL, Ornellas JS, de Oliveira FP. Technical error of measurement in anthropometry. Rev Bras Med Esporte 2005;11:86-90.
Weinberg SM, Scott NM, Neiswanger K, Brandon CA, Marazita ML. Digital three-dimensional photogrammetry: Evaluation of anthropometric precision and accuracy using a genex3D camera system. Cleft Palate Craniofac J 2004;41:507-18.
Shrout PE, Fleiss JL. Intraclass correlations: Uses in assessing rater reliability. Psychol Bull 1979;86:420-8.
Farkas LG, Katic MJ, Forrest CR, Alt KW, Bagic I, Baltadjiev G, et al.
International anthropometric study of facial morphology in various ethnic groups/races. J Craniofac Surg 2005;16:615-46.
Reddy BM, Chopra VP, Karmakar B, Malhotra KC, Mueller H. Quantitative dermatoglyphics and population structure in northwest India. Am J Hum Biol 2000;12:315-26.
Karmakar B, Yakovenko K, Kobyliansky E. 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 2006;18:377-86.
Siváková D, Scheil HG, Schmidt HD, Vulpe C. Population affinities assessed by dermatoglyphic and hemogenetic variables. Anthropol Anz 2007;65:137-46.
Karmakar B, Yakovenko K, Kobyliansky E. Sexual dimorphism in the Chuvashian population of Russia in two types of dermatoglyphic traits: Principal component analysis. Coll Antropol 2008;32:467-77.
Adamu LH, Ojo SA, Danborno B, Adebisi SS, Taura MG. Evaluation of facial proportions and its association with thumbprint patterns among Hausa ethnic group. J Anthropol 2017. doi.org/10.1155/2017/9571470. Available from: https://www.hindawi.com/journals/janthro/aip/9571470/
. [Last accessed on 2017 Dec 06].
Krishan K, Ghosh A, Kanchan T, Ngangom C, Sen J. Sex differences in fingerprint ridge density - Causes and further observations. J Forensic Leg Med 2010;17:172-3.
Kapoor N, Badiye A. Sex differences in thumbprint ridge density in a central Indian population. Egyptian J Forensic Sci 2015;5:23-9.
Zhuang Z, Landsitte D, Benson S, Roberge R, Shaffer R. Facial anthropometric differences among gender, ethnicity, and age groups. Ann Occup Hyg 2010;54:391-402.
Ozkaya N, Sagiroglu S. Face recognition from fingerprints. J Fac Eng Archit Gazi 2008;23:785-94.
Ozkaya N, Sagiroglu S. 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 2008, Hong Kong; 2008.
Ozkaya N, Sagiroglu S. Translating the Fingerprints to the Faces: A New Approach. IEEE 16th
Signal Processing, Communication and Applications Conference (Siu 2008), IEEE Catalog Number: CFP08559 - CDR. Library of Congress: 2007943521, 20-22 April 2008, Turkey; 2008.
Ozkaya N, Sagiroglu S. Generating one biometric feature from another: Faces from fingerprints. Sensors (Basel) 2010;10:4206-37.
Jantz RL, Owsley DW. Factor analysis of finger ridge-counts in blacks and whites. Ann Hum Biol 1977;4:357-66.
Kucken M, Newell AC. A model of fingerprints formation. Eur Phys Latt 2004;68:141-6.
Corey M. 450 Year Old Fingerprints Found from ST. John's Bahamas Wreck! Mel Fisher Maritime Heritage Society; 2002. Available from: http://www.melfisher.org/fingerprints.htm
. [Last accessed on 2017 Dec 06].
Králík M, Novotný V. Epidermal ridge breadth: An indicator of age and sex in paleodermatoglyphics. Variab Evol 2003;11:5-30.
Coetzee V, Perrett DI, Stephen LD. Facial adiposity: A cue to health? Perception 2009;38:1700-11.
Coetzee V, Perrett DI, Stephen LD. Deciphering faces: Quantifiable visual cues to weight. Perception 2010;39:51-61.
Duong Duc P, Jun-Hyeong D, Boncho K, Hae Jung L, Honggie K, Jong Yeol K. Body mass index and facial cues in sasang typology for young and elderly persons. Evid Based Complement Alternat Med 2011;1-9. doi.org/10.1155/2011/749209. Available from: http://dx.doi.org/10.1155/2011/749209
. [Last accessed on 2017 Dec 06].
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7]