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Estimation of stature from handprint dimensions in Egyptian population
Accepted Manuscript Estimation of stature from handprint dimensions in Egyptian population Melad G. Paulis PII:
S1752-928X(15)00098-0
DOI:
10.1016/j.jflm.2015.05.007
Reference:
YJFLM 1171
To appear in:
Journal of Forensic and Legal Medicine
Received Date: 10 February 2015 Revised Date:
1 May 2015
Accepted Date: 20 May 2015
Please cite this article as: Paulis MG, Estimation of stature from handprint dimensions in Egyptian population, Journal of Forensic and Legal Medicine (2015), doi: 10.1016/j.jflm.2015.05.007. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Melad G. Paulis
Estimation of stature from handprint dimensions in Egyptian population Forensic Medicine and Clinical Toxicology, Faculty of Medicine, Minia University, Egypt E-mail: [email protected]
Address: Forensic Medicine and Clinical Toxicology Department. Faculty of Medicine. Minia University. Minia
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Tel: 02/01222993067
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Estimation of stature from handprint dimensions in Egyptian population
Abstract:
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Handprint in the scene of the crime is one of the most valuable clues in identification of the assailant. There are numerous studies on estimation of stature from direct measures of hand dimensions, but using a handprint instead, there is little research on it. So this study tried to focus on handprint as a tool used in estimation of stature. One hundred right male hands and 91 right female hands were scanned, processed via Photoshop program and handprint measurements were taken using a software program. Our results showed that stature could be estimated from handprint measurements by simple and multiple regression equations with standard error of estimate was the lowest in handprint length ±4.54 cm in male and ±5.38 cm in female. It was concluded that handprint from the scene of the crime could be used for the prediction of the stature of the assailant.
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Keywords: Stature, Hand anthropometry, Handprints, Regression analysis, Egyptians.
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Introduction
Personal identification plays a vital role in medico-legal and crime scene investigation. In this context, stature is considered as one of the ‘‘Big Four’’ parameters required to assist with the identification of an individual when other lines of evidence are corroborative [1-3]. There are numerous publications describing anthropometric approaches to estimating stature from different body parts, for example head dimensions [4-6], lower limb [7,8)], upper limb bone [9,10]. Various studies estimated stature from hand dimensions either directly from fleshy hands [11,12], or from hand bone [13,14]. Two prints also were used, namely foot and handprint. Footprint has gained more focus for estimation of stature than that of hand print [15-21]. Only a few works were done in the handprint [3].
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In contrast to methods depend on direct measurement from hand in which most cases the complete hand is available, Ahemad & Purkait, 2011 [3] stated that handprint left varied with the type of activities the person is engaged in. Also, usually partial impression of the hand is left. Last, landmarks of the hand may be not visible in all cases. These notes make the use of a handprint in the estimation of stature is more difficult, especially with the addition of techniques used discover handprint in the scene of crime.
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Finger and palm prints are some of the most valuable clues at the crime scene. Often, the only evidence that may be available at the scene of a crime is in the form of latent impressions of hands. Prints are conclusive evidence. When criminals work, they cannot avoid leaving clues in the form of fingerprints unless they wear gloves or some other form of protection [22]. Stature calculation from theses prints may support height estimation of suspects made by eye-witnesses or narrow down the pool of suspects (23). Also, in the recent decade, increasing interest has been paid for the reconstruction of the biological identity of individuals who left handprints in the prehistoric caves and rock shelters around the world [24]. Hence come the importance of more research on estimation of stature from handprint which help in anthropology, medicolegal and crime scene investigations. The present research was thus, conducted with an aim to find if stature could be predicted from handprint dimensions in Egyptian population sample with the help of a computer program tool in taking these measures. Material and methods Materials This study was carried out in the Forensic Medicine and Clinical Toxicology Department and Minia University Hospital, Faculty of Medicine, Minia University from March to September 2014. It was conducted on
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191 (100 male and 91 female) volunteers from patients’ relatives visiting Minia University Hospital outpatient clinics. Ages of participants were above 18 years old. Several studies have found that there were insignificant differences between right and left hand measures so only right hands of right handed persons were included in this study [25-29]. An informed written consents were taken from all participants and the followed procedures were in accordance with the ethical standards of El-Minia University committee on human experimentation. Only individuals without any medical history of hand, foot and backbone problems were recruited for the study.
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Method Handprint acquisition
A flatbed scanner (HP Scanjet 200), retrofitted with a custom mounting box (to standardize light and hand position) and scale was used to acquire images of the hand at 300dpi [30]. Adobe Photoshop (SC6 64bit edition) software package was used in editing the image to obtain the most accurate approximation of a handprint.
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Measurements
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An anthropometric rod was used for the measurement of stature. Stature was taken according to Habib and Kamal [25]. In brief, the subject stood barefoot on a flat surface. The anthropometer was placed in a straight vertical position in front of the subject with the head oriented in eye–ear–eye plane (Frankfurt plane). Feet axis was parallel or slightly divergent and hands hung down. The movable rod of the anthropometer was brought into contact with the vertex in the mid sagittal plane. The measurements were repeated and the mean measures were recorded (by one observer) in order to avoid inter-observer errors. All measures were recorded in centimeters to the nearest millimeters.
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Measurements of handprint were taken by a computer program specialized in measuring distance in images. It is called Klonk Image Measurement version 14.2.1.5. Calibration of the length was done using a scale put in the scanner during handprint acquisition. The program affords option of saving the recoded measurement length directly in excel file so it decrease the time and error if manual manipulation of data was used. The following handprint measures were taken: Phalangeal length: The phalange length was measured as the distance between the centers of two phalange creases. The distal phalange length was the distance between the most forwarding projecting point on the tip of a finger to the first distal phalange crease (Fig 1).
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Handprint breadth (HB) was measured as the distance from the most laterally projected part of the palm print at the second metacarpal to the most medially projected part of the palm print at the distal transverse crease. Handprint length (HL) was measured as the distance from the baseline of the print (transverse line from the most inferior a point of the medial border of the palm) to the tip of the middle finger [30] (Fig 2).
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Twenty cases of male and female handprints were measured manually and using Klonk Image Measurement software. Results of manual measurement and computerized measurement were compared using t-test. No significanct differences were found between the two results in both male and female. The data were analyzed using the statistical package of social sciences (SPSS) version 22, and regression formulae were calculated for various combinations in order to reach the best estimate possible. Results
Age distribution among the 100 examined male individuals was 34.2±13.95 years while among 91 female participants was 35.1 ± 9.99 years with a range from 18 to 67 years in all participants. As it appears from table 1; men are significantly taller than female in the studied sample with a stature mean of male 167.89±5.86 cm while in female 156.96 ± 6.64 cm. All the taken measures show significant differences between male and female (p value < 0.05). Also, from table (1); it shows that all measurements of men’s handprint are larger than the corresponding females.
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Table 2 and 3 describe the correlation coefficient between stature and different hand and phalanges print measurements in male and female. All measures of male handprint show significant correlations except L3 and T2. The highest correlation is found to be between stature and HL (r=.618). It is followed by M1, I3, and I1 (r= 0.612, 0.529 and 0.503 respectively). As regards female handprint measures; all the measurements give a significant correlation coefficient with stature except L2 and T1 (p value is 0.693 and 0.961 respectively). The highest correlation coefficient is between stature and HL (r = 0.412). All correlation coefficients between stature and anthropometric measurements are higher in male than the corresponding measurements female. Table 2 illustrates simple linear regression equations for estimation of stature from each individual variable in male participants. Standard error of estimate (SEE) (which reflects the deviation of the estimated stature from the equation from that of the actual one) shows slight differences between different used anthropometric measurements. It is being the highest in R3 (±5.89 cm) and lowest in HL (±4.54).
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In a similar manner; in table 3, equations are developed for each measurement of handprint in female participants. SEE shows the lowest deviation when HL is used in stature estimation (±5.38 cm).
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Using multiple regression analysis (stepwise); there is much improvement in estimating stature when using more than one measure. This is true for both male and female handprint measures. Table 4 shows that male stature could be estimated with SEE low to ±1.67 cm when 13 measurements were used. SEE increases when a less number of measurements are used to be ±3.04, ±3.76 and ±4.19 cm (7, 4 and 2 measurements are used in each case respectively). As regards stepwise regression analysis in female handprint measurements (table 4), it gives much improvement in accuracy of estimating stature in comparison with simple regression analysis (table 4). SEE becomes small as ± 1.84 cm when 7 measurements were used. It increases to ±3.04, ±3.49 and ±4.79 cm when 5, 4 and 3 measurements were used respectively. Discussion
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For over one hundred years, handprints have been routinely used by law enforcement agencies throughout the world to identify suspects. This is true in comparing handprint found at the crime scene and the suspect. But when there is no suspect to compare; could handprint be helpful to give an idea about the accused? This is what this work tries to answer.
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There are many studies on estimation of stature from direct measurement of the hands on various populations; Indian [31, 32]; Japanese [33]; Mauritanian [34], Nigerian [35] and Western Australian [30]. Also, there are 2 studies that were done on Egyptian population samples [25, 36].
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The use of measurement of handprint has started to attain researchers’ attention few years earlier. Sharma and Kapoor [37] reported estimation of stature from fingertip length and fingerprint tip length among criminals. This was followed by Jasuja & Singh [23] who measured the hand length and breadth of the inked impression of the hand. In 2011 Ahemad & Purkait [3] used inked impression of male participants only. Lastly, in 2012, to avoid ink staining of the hand, Ishak et al [30] used scanned images of the hand instead of the ink impression and manually measured hand and finger dimensions of the printed copies of these images. Instead of manual measurement, a computer software was used in this study to measure hand and phalanges dimensions of scanned hand pictures. On comparing our results with those done in Egyptian population samples, it was found that the mean of the stature in our sample (167.89±5.86 cm for male and 156.96±6.64 cm for female) was smaller than that of Habib & Kamal [25] (174.61±7.34, 160±5.45 cm) and Abdel-Malek et al [36] (172.8± 7.2, 158.9 ±5.37 cm). This may be attributed to a wide range of age included in this study. Guerra et al [34] Cline et al [39], and Milne & Williamson [40] mentioned that with increasing age, a narrowing of the spinal discs and a decrease in the spine length occurs. The right handprint measures of male participants are larger than the female ones and this goes in line with studies on samples from the same populations [25, 26, 36], and other populations [27, 23, 37, 42].
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The correlation coefficient between handprint measures and stature was higher in male than female in both. These results agree with studies on the Egyptians [25, 36] and other populations [23, 41, 42].
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Simple regression analysis of our study showed that stature can be estimated from handprint measurements in both male and female. HL is the most accurate single parameter in the estimation of stature. This is reflected from its lower SEE value (±4.54 in male and ± 5.38 cm in female). This was similar to that found by several studies on direct measurements of the hand [25, 29, 30, 33] and handprint measurements [3, 30]. It is worthy to mention that Habib & Kamal [25] found that SEE of stature from HL was slightly better as regard female, but, less accurate as regard male (SEE ±5.30 in male and ±4.77 in female). This may be attributed to different landmarks used in both studies [Habib & Kamal [25] used wrist crease). Ishak et al [30] found that handprint measurements give less accurate results than direct measurements of the hand, but, Jasuja & Singh [23] found that there were more or less similar results between handprint and direct measurements of the hand.
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Multiple stepwise regression analysis using more than one variable together resulted in more improvement in the accuracy of estimation of stature in both sexes. This was manifested by a decrease in SEE. In male SEE was as small as ± 1.67 cm when thirteen of the sixteen measures taken were included in the equation. With less number of measurements were used, the accuracy of stature estimation was less. It became ± 4.19 cm when only HL and I3. Multiple stepwise regression analysis in female gave similar but still less accurate results than that of male. These results are similar findings in other studies in which multiple regression analysis improved the accuracy of the estimation of stature [25, 30, 43]. From this study, it is concluded that prints left by the hand, or even parts of it, in the scene of the crime can be used to estimate the height of an unknown suspect. This may be used to help narrow down suspect profiles given out to the public or could be used to narrow down a given list of suspects. A novel method could be used using a software program instead of manual measuring. This can save time and effort needed to take measures. Conflict of interest
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36. Abdel-Malek AK, Ahmed AM, El Sharkawp SA, El Hamidb NA. Prediction of stature from hand measurements. Forensic Science International.1990; 46 (3):181- 187. 37. Sharma PK, Kapoor AK. Estimation of stature from fingertip length and finger print tip length among criminals in: Recent advances in forensic biology, Kamla-Raj Publishers; 2001. 38. Guerra RS, Fonseca I, Pichel F, Restivo MT, Amaral TF. Hand length as an alternative measurement of height. European Journal of Clinical Nutrition. 2014; 68:229–233 39. Cline MG, Meredith KE, Boyer JT, Burrows B. Decline of height with age in adults in a general population sample: estimating maximum height and distinguishing birth cohort effects from actual loss of stature with aging. Hum Biol. 1989; 61:415–425. 40. Milne JS, Williamson J. A longitudinal study of kyphosis in older people. Age Ageing. 1983; 12: 225– 233. 41. Krishan K, Kanchan T, Sharma A. Multiplication factor versus regression analysis in stature estimation from hand and foot dimensions. Journal of Forensic and Legal Medicine. 2012; 19(4):211-214. 42. Ozaslan A, Karadayi B, Kolusayin MO, Kaya A, Afsin H. Predictive role of hand and foot dimensions in stature estimation. Rom J Leg Med. 2012; 20:41-46. 43. Sen J, Kanchan T, Ghosh A, Mondal N, Krishan K. Estimation of stature from lengths of index and ring fingers in a North-eastern Indian population. Journal of Forensic and Legal Medicine. 2014; (22): 10-5
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Table (1): Descriptive statistics for stature and right handprint measurements in male and female participants. Female
t-values (sex differences) * Mean SD SEM Mean SD SEM T P Stature 167.89 5.86 0.586 156.96 6.64 0.697 12.073 .000 a L1 2.48 0.12 0.012 2.33 0.26 0.027 5.110 .000 a L2 1.87 0.21 0.021 1.77 0.22 0.023 3.238 .001 a L3 2.07 0.20 0.020 1.93 0.18 0.018 5.283 .000 b R1 2.67 0.14 0.014 2.56 0.28 0.029 3.587 .000 b R2 2.49 0.27 0.027 2.37 0.24 0.026 3.353 .001 b R3 2.53 0.20 0.020 2.40 0.23 0.024 4.109 .000 c M1 2.67 0.15 0.015 2.57 0.29 0.030 2.907 .004 c M2 2.68 0.29 0.029 2.48 0.25 0.026 5.189 .000 c M3 2.82 0.13 0.013 2.71 0.27 0.028 3.774 .000 d I1 2.58 0.17 0.017 2.48 0.29 0.030 3.044 .003 d I2 2.41 0.18 0.018 2.17 0.22 0.023 8.372 .000 d I3 2.33 0.21 0.021 2.22 0.21 0.022 5.725 .001 T1e 3.33 0.35 0.035 2.99 0.35 0.036 6.683 .000 e T2 3.28 0.41 0.041 3.06 0.40 0.042 3.856 .000 f HB 8.73 0.40 0.040 8.07 0.73 0.077 7.858 .000 g HL 19.12 0.91 0.093 17.82 1.57 0.165 6.937 .000 a L1, L2 and L3: distal, middle and proximal phalange print length of little finger respectively.
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Male
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R1, R2 and R3: distal, middle and proximal phalange print length of ring finger respectively.
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M1, M2 and M3: distal, middle and proximal phalange print length of middle finger respectively.
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I1, I2 and I3: distal, middle and proximal phalange print length of index finger respectively. T1 and T2: distal and proximal phalange print length of the thumb respectively.
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HB: handprint breadth.
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HL: handprint length.
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Significant at p < 0.05.
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All measures in cm.
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Table (2): Correlation coefficient (between stature and handprint measures) and simple linear regression equations for estimation of stature (in cm) from measurements of handprints and phalange prints on male participants.
simple linear regression
S = stature
˟ Correlation is significant at the 0.05 level (2-tailed).
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˟˟ Correlation is significant at the 0.01 level (2-tailed). P < 0.05 is significant.
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R 0.494 0.245 0.134 0.449 0.333 0.031 0.611 0.404 0.387 0.338 0.338 0.529 0.252 0.071 0.422 0.519
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R .244 .06 .018 .202 .111 .001 .375 .163 .150 .114 .114 .280 .064 .005 .178 .269
P .000 .014 .182 .000 .001 .000 .000 .000 .000 .000 .001 .000 .011 .486 .000 .000
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SEE ±5.12 ±5.71 ±5.83 ±5.26 ±5.55 ±5.89 ±4.66 ±5.39 ±5.43 ±5.54 ±5.54 ±4.99 ±5.70 ±5.87 ±5.33 ±4.54
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Equation S= 110.37 + 23.17 L1 S= 155.15 + 6.81 L2 S= 159.77 + 3.92 L3 S= 117.69 + 18.78 R1 S= 149.60 + 7.34 R2 S= 170.01 - 0.84 R3 S= 102.25 + 24.60 M1 S= 145.71 + 8.26 M2 S= 120.13 + 16.93 M3 S= 122.41 +17.63 I1 S= 140.87 + 11.20 I2 S= 134.27 + 14.46 I3 S= 153.77 + 4.24 T1 S= 164.55 + 1.02 T2 S=113.658 + 6.213 S=109.533 + 3.018
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L1 L2 L3 R1 R2 R3 M1 M2 M3 I1 I2 I3 T1 T2 HB HL
Correlation coefficient r .449˟˟ .245˟ .134 .494˟˟ .333˟˟ .029 .612˟˟ .403˟˟ .387˟˟ .503˟˟ .338˟˟ .529˟˟ .252˟ .071 .422˟˟ .618˟˟
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Table (3): Correlation coefficient (between stature and handprint measures) and simple linear regression equations for estimation of stature (in cm) from measurements of handprints and phalange prints on female participants. Simple linear regression R .228 .045 .134 .187 .032 .214 .122 .161 .170 .148 .389 .281 .032 .219 .141 .298
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R .052 .002 .018 .035 .001 .046 .015 .026 .029 .022 .151 .079 .001 .048 .020 .089
p .029 .693 .01 .000 .01 .042 .01 .02 .01 .01 .000 .007 .961 .037 .02 .004
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SEE ± 6.50 ± 6.68 ± 6.62 ± 6.56 ± 6.68 ± 6.52 ± 6.63 ± 6.59 ± 6.58 ± 6.61 ± 6.16 ± 6.41 ± 6.68 ± 6.52 ± 6.61 ± 5.38
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Equation S= 143.346+ 5.836 S= 159.171 -1.249 S= 166.853 -5.134 S= 145.459+ 4.494 S= 156.504+ .193 S= 142.254+ 6.123 S= 149.597+ 2.862 S= 146.333+ 4.283 S= 145.351+ 4.287 S= 148.520+ 3.409 S= 131.247+ 11.832 S= 136.106+ 8.767 S= 156.668+ .098 S= 145.755+ 3.665 S= 146.561+ 1.290 S= 134.516 + 1.259
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L1 L2 L3 R1 R2 R3 M1 M2 M3 I1 I2 I3 T1 T2 HB HL
Correlation coefficient r .229˟ .042 .135 ˟ .188 ˟˟ .051 ˟ .213˟ .124 ˟ .161˟ .171˟ .149 ˟ .389˟˟ .281˟˟ .005 .219˟ .142˟ .412˟˟ S = stature
˟ Correlation is significant at the 0.05 level (2-tailed).
˟˟ Correlation is significant at the 0.01 level (2-tailed).
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P < 0.05 is significant.
Table (4): Stepwise regression equations for estimation of stature (in cm) from handprint measurements in both male and female participants.
female
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Male
Equation S= 88.15 + 8.60 HB + 14.72 I3 -3.331M2 -12.232L2 +37.231 I2 +33.012R2 - 9.385 L1 + 1.626 HL -54.256 R1 + 28.724 M1 -13.928 R3 -6.758 T2 + 19.171 I2 -10.317 L3 S= 79.380 + 7.211 HB + 12.083 I3 -18.432 L2 + 14.076 I2 + 6.464 R2 - 25.184 L1 + 2.270 HL S= 95.632 + 4.976 HB + 8.819 I3 + 8.658 M2 - 8.299 L2 S= 96.595 + 5.876 HB + 8.371 I3 S= 155.221 + 20.513 I1 - 54.551L3 + 4.416 HL - 24.438 HB + 30.154 M3 + 20.488 I3 + 19.631 R1 S= 152.011 + 25.082 I2 - 42.729 L3 + 6.648 HL - 16.455 HB + 17.353 M3 S= 149.068 + 25.246 I2 - 36.324 L3 + 6.676 HL - 11.901 HB S= 145.309 + 12.775 I2 - 35.484 L3 + 2.932 HL
S = stature
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SEE 1.67
R2 .915
3.04
.693
3.76 4.19 1.84
.516 .383 .929
3.04
.802
3.49 4.79
.736 .497
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Fig. 1 Measurements of phalangeal length of handprint using Klonk Image Measurement software. L1, L2 and L3: distal, middle and proximal phalange print length of little finger respectively. R1, R2 and R3: distal, middle and proximal phalange print length of ring finger respectively.
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M1, M2 and M3: distal, middle and proximal phalange print length of middle finger respectively. I1, I2 and I3: distal, middle and proximal phalange print length of index finger respectively.
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T1 and T2: distal and proximal phalange print length of the thumb respectively.
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Fig. 2 Measurements of handprint length and breadth using Klonk Image Measurement software. HL: Handprint length.
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HB: Handprint breadth.
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Highlights Handprint play vital role in both anthropology and crime scene investigation. Scanned hands of both male and female from Egyptian population sample was modified to simulate handprint and different measures were recoded. Using simple and stepwise regression analysis, stature could be predicted from handprint measurements.
S1752-928X(15)00098-0
DOI:
10.1016/j.jflm.2015.05.007
Reference:
YJFLM 1171
To appear in:
Journal of Forensic and Legal Medicine
Received Date: 10 February 2015 Revised Date:
1 May 2015
Accepted Date: 20 May 2015
Please cite this article as: Paulis MG, Estimation of stature from handprint dimensions in Egyptian population, Journal of Forensic and Legal Medicine (2015), doi: 10.1016/j.jflm.2015.05.007. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Melad G. Paulis
Estimation of stature from handprint dimensions in Egyptian population Forensic Medicine and Clinical Toxicology, Faculty of Medicine, Minia University, Egypt E-mail: [email protected]
Address: Forensic Medicine and Clinical Toxicology Department. Faculty of Medicine. Minia University. Minia
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Tel: 02/01222993067
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Estimation of stature from handprint dimensions in Egyptian population
Abstract:
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Handprint in the scene of the crime is one of the most valuable clues in identification of the assailant. There are numerous studies on estimation of stature from direct measures of hand dimensions, but using a handprint instead, there is little research on it. So this study tried to focus on handprint as a tool used in estimation of stature. One hundred right male hands and 91 right female hands were scanned, processed via Photoshop program and handprint measurements were taken using a software program. Our results showed that stature could be estimated from handprint measurements by simple and multiple regression equations with standard error of estimate was the lowest in handprint length ±4.54 cm in male and ±5.38 cm in female. It was concluded that handprint from the scene of the crime could be used for the prediction of the stature of the assailant.
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Keywords: Stature, Hand anthropometry, Handprints, Regression analysis, Egyptians.
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Introduction
Personal identification plays a vital role in medico-legal and crime scene investigation. In this context, stature is considered as one of the ‘‘Big Four’’ parameters required to assist with the identification of an individual when other lines of evidence are corroborative [1-3]. There are numerous publications describing anthropometric approaches to estimating stature from different body parts, for example head dimensions [4-6], lower limb [7,8)], upper limb bone [9,10]. Various studies estimated stature from hand dimensions either directly from fleshy hands [11,12], or from hand bone [13,14]. Two prints also were used, namely foot and handprint. Footprint has gained more focus for estimation of stature than that of hand print [15-21]. Only a few works were done in the handprint [3].
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In contrast to methods depend on direct measurement from hand in which most cases the complete hand is available, Ahemad & Purkait, 2011 [3] stated that handprint left varied with the type of activities the person is engaged in. Also, usually partial impression of the hand is left. Last, landmarks of the hand may be not visible in all cases. These notes make the use of a handprint in the estimation of stature is more difficult, especially with the addition of techniques used discover handprint in the scene of crime.
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Finger and palm prints are some of the most valuable clues at the crime scene. Often, the only evidence that may be available at the scene of a crime is in the form of latent impressions of hands. Prints are conclusive evidence. When criminals work, they cannot avoid leaving clues in the form of fingerprints unless they wear gloves or some other form of protection [22]. Stature calculation from theses prints may support height estimation of suspects made by eye-witnesses or narrow down the pool of suspects (23). Also, in the recent decade, increasing interest has been paid for the reconstruction of the biological identity of individuals who left handprints in the prehistoric caves and rock shelters around the world [24]. Hence come the importance of more research on estimation of stature from handprint which help in anthropology, medicolegal and crime scene investigations. The present research was thus, conducted with an aim to find if stature could be predicted from handprint dimensions in Egyptian population sample with the help of a computer program tool in taking these measures. Material and methods Materials This study was carried out in the Forensic Medicine and Clinical Toxicology Department and Minia University Hospital, Faculty of Medicine, Minia University from March to September 2014. It was conducted on
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191 (100 male and 91 female) volunteers from patients’ relatives visiting Minia University Hospital outpatient clinics. Ages of participants were above 18 years old. Several studies have found that there were insignificant differences between right and left hand measures so only right hands of right handed persons were included in this study [25-29]. An informed written consents were taken from all participants and the followed procedures were in accordance with the ethical standards of El-Minia University committee on human experimentation. Only individuals without any medical history of hand, foot and backbone problems were recruited for the study.
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Method Handprint acquisition
A flatbed scanner (HP Scanjet 200), retrofitted with a custom mounting box (to standardize light and hand position) and scale was used to acquire images of the hand at 300dpi [30]. Adobe Photoshop (SC6 64bit edition) software package was used in editing the image to obtain the most accurate approximation of a handprint.
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Measurements
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An anthropometric rod was used for the measurement of stature. Stature was taken according to Habib and Kamal [25]. In brief, the subject stood barefoot on a flat surface. The anthropometer was placed in a straight vertical position in front of the subject with the head oriented in eye–ear–eye plane (Frankfurt plane). Feet axis was parallel or slightly divergent and hands hung down. The movable rod of the anthropometer was brought into contact with the vertex in the mid sagittal plane. The measurements were repeated and the mean measures were recorded (by one observer) in order to avoid inter-observer errors. All measures were recorded in centimeters to the nearest millimeters.
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Measurements of handprint were taken by a computer program specialized in measuring distance in images. It is called Klonk Image Measurement version 14.2.1.5. Calibration of the length was done using a scale put in the scanner during handprint acquisition. The program affords option of saving the recoded measurement length directly in excel file so it decrease the time and error if manual manipulation of data was used. The following handprint measures were taken: Phalangeal length: The phalange length was measured as the distance between the centers of two phalange creases. The distal phalange length was the distance between the most forwarding projecting point on the tip of a finger to the first distal phalange crease (Fig 1).
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Handprint breadth (HB) was measured as the distance from the most laterally projected part of the palm print at the second metacarpal to the most medially projected part of the palm print at the distal transverse crease. Handprint length (HL) was measured as the distance from the baseline of the print (transverse line from the most inferior a point of the medial border of the palm) to the tip of the middle finger [30] (Fig 2).
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Twenty cases of male and female handprints were measured manually and using Klonk Image Measurement software. Results of manual measurement and computerized measurement were compared using t-test. No significanct differences were found between the two results in both male and female. The data were analyzed using the statistical package of social sciences (SPSS) version 22, and regression formulae were calculated for various combinations in order to reach the best estimate possible. Results
Age distribution among the 100 examined male individuals was 34.2±13.95 years while among 91 female participants was 35.1 ± 9.99 years with a range from 18 to 67 years in all participants. As it appears from table 1; men are significantly taller than female in the studied sample with a stature mean of male 167.89±5.86 cm while in female 156.96 ± 6.64 cm. All the taken measures show significant differences between male and female (p value < 0.05). Also, from table (1); it shows that all measurements of men’s handprint are larger than the corresponding females.
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Table 2 and 3 describe the correlation coefficient between stature and different hand and phalanges print measurements in male and female. All measures of male handprint show significant correlations except L3 and T2. The highest correlation is found to be between stature and HL (r=.618). It is followed by M1, I3, and I1 (r= 0.612, 0.529 and 0.503 respectively). As regards female handprint measures; all the measurements give a significant correlation coefficient with stature except L2 and T1 (p value is 0.693 and 0.961 respectively). The highest correlation coefficient is between stature and HL (r = 0.412). All correlation coefficients between stature and anthropometric measurements are higher in male than the corresponding measurements female. Table 2 illustrates simple linear regression equations for estimation of stature from each individual variable in male participants. Standard error of estimate (SEE) (which reflects the deviation of the estimated stature from the equation from that of the actual one) shows slight differences between different used anthropometric measurements. It is being the highest in R3 (±5.89 cm) and lowest in HL (±4.54).
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In a similar manner; in table 3, equations are developed for each measurement of handprint in female participants. SEE shows the lowest deviation when HL is used in stature estimation (±5.38 cm).
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Using multiple regression analysis (stepwise); there is much improvement in estimating stature when using more than one measure. This is true for both male and female handprint measures. Table 4 shows that male stature could be estimated with SEE low to ±1.67 cm when 13 measurements were used. SEE increases when a less number of measurements are used to be ±3.04, ±3.76 and ±4.19 cm (7, 4 and 2 measurements are used in each case respectively). As regards stepwise regression analysis in female handprint measurements (table 4), it gives much improvement in accuracy of estimating stature in comparison with simple regression analysis (table 4). SEE becomes small as ± 1.84 cm when 7 measurements were used. It increases to ±3.04, ±3.49 and ±4.79 cm when 5, 4 and 3 measurements were used respectively. Discussion
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For over one hundred years, handprints have been routinely used by law enforcement agencies throughout the world to identify suspects. This is true in comparing handprint found at the crime scene and the suspect. But when there is no suspect to compare; could handprint be helpful to give an idea about the accused? This is what this work tries to answer.
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There are many studies on estimation of stature from direct measurement of the hands on various populations; Indian [31, 32]; Japanese [33]; Mauritanian [34], Nigerian [35] and Western Australian [30]. Also, there are 2 studies that were done on Egyptian population samples [25, 36].
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The use of measurement of handprint has started to attain researchers’ attention few years earlier. Sharma and Kapoor [37] reported estimation of stature from fingertip length and fingerprint tip length among criminals. This was followed by Jasuja & Singh [23] who measured the hand length and breadth of the inked impression of the hand. In 2011 Ahemad & Purkait [3] used inked impression of male participants only. Lastly, in 2012, to avoid ink staining of the hand, Ishak et al [30] used scanned images of the hand instead of the ink impression and manually measured hand and finger dimensions of the printed copies of these images. Instead of manual measurement, a computer software was used in this study to measure hand and phalanges dimensions of scanned hand pictures. On comparing our results with those done in Egyptian population samples, it was found that the mean of the stature in our sample (167.89±5.86 cm for male and 156.96±6.64 cm for female) was smaller than that of Habib & Kamal [25] (174.61±7.34, 160±5.45 cm) and Abdel-Malek et al [36] (172.8± 7.2, 158.9 ±5.37 cm). This may be attributed to a wide range of age included in this study. Guerra et al [34] Cline et al [39], and Milne & Williamson [40] mentioned that with increasing age, a narrowing of the spinal discs and a decrease in the spine length occurs. The right handprint measures of male participants are larger than the female ones and this goes in line with studies on samples from the same populations [25, 26, 36], and other populations [27, 23, 37, 42].
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The correlation coefficient between handprint measures and stature was higher in male than female in both. These results agree with studies on the Egyptians [25, 36] and other populations [23, 41, 42].
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Simple regression analysis of our study showed that stature can be estimated from handprint measurements in both male and female. HL is the most accurate single parameter in the estimation of stature. This is reflected from its lower SEE value (±4.54 in male and ± 5.38 cm in female). This was similar to that found by several studies on direct measurements of the hand [25, 29, 30, 33] and handprint measurements [3, 30]. It is worthy to mention that Habib & Kamal [25] found that SEE of stature from HL was slightly better as regard female, but, less accurate as regard male (SEE ±5.30 in male and ±4.77 in female). This may be attributed to different landmarks used in both studies [Habib & Kamal [25] used wrist crease). Ishak et al [30] found that handprint measurements give less accurate results than direct measurements of the hand, but, Jasuja & Singh [23] found that there were more or less similar results between handprint and direct measurements of the hand.
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Multiple stepwise regression analysis using more than one variable together resulted in more improvement in the accuracy of estimation of stature in both sexes. This was manifested by a decrease in SEE. In male SEE was as small as ± 1.67 cm when thirteen of the sixteen measures taken were included in the equation. With less number of measurements were used, the accuracy of stature estimation was less. It became ± 4.19 cm when only HL and I3. Multiple stepwise regression analysis in female gave similar but still less accurate results than that of male. These results are similar findings in other studies in which multiple regression analysis improved the accuracy of the estimation of stature [25, 30, 43]. From this study, it is concluded that prints left by the hand, or even parts of it, in the scene of the crime can be used to estimate the height of an unknown suspect. This may be used to help narrow down suspect profiles given out to the public or could be used to narrow down a given list of suspects. A novel method could be used using a software program instead of manual measuring. This can save time and effort needed to take measures. Conflict of interest
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36. Abdel-Malek AK, Ahmed AM, El Sharkawp SA, El Hamidb NA. Prediction of stature from hand measurements. Forensic Science International.1990; 46 (3):181- 187. 37. Sharma PK, Kapoor AK. Estimation of stature from fingertip length and finger print tip length among criminals in: Recent advances in forensic biology, Kamla-Raj Publishers; 2001. 38. Guerra RS, Fonseca I, Pichel F, Restivo MT, Amaral TF. Hand length as an alternative measurement of height. European Journal of Clinical Nutrition. 2014; 68:229–233 39. Cline MG, Meredith KE, Boyer JT, Burrows B. Decline of height with age in adults in a general population sample: estimating maximum height and distinguishing birth cohort effects from actual loss of stature with aging. Hum Biol. 1989; 61:415–425. 40. Milne JS, Williamson J. A longitudinal study of kyphosis in older people. Age Ageing. 1983; 12: 225– 233. 41. Krishan K, Kanchan T, Sharma A. Multiplication factor versus regression analysis in stature estimation from hand and foot dimensions. Journal of Forensic and Legal Medicine. 2012; 19(4):211-214. 42. Ozaslan A, Karadayi B, Kolusayin MO, Kaya A, Afsin H. Predictive role of hand and foot dimensions in stature estimation. Rom J Leg Med. 2012; 20:41-46. 43. Sen J, Kanchan T, Ghosh A, Mondal N, Krishan K. Estimation of stature from lengths of index and ring fingers in a North-eastern Indian population. Journal of Forensic and Legal Medicine. 2014; (22): 10-5
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Table (1): Descriptive statistics for stature and right handprint measurements in male and female participants. Female
t-values (sex differences) * Mean SD SEM Mean SD SEM T P Stature 167.89 5.86 0.586 156.96 6.64 0.697 12.073 .000 a L1 2.48 0.12 0.012 2.33 0.26 0.027 5.110 .000 a L2 1.87 0.21 0.021 1.77 0.22 0.023 3.238 .001 a L3 2.07 0.20 0.020 1.93 0.18 0.018 5.283 .000 b R1 2.67 0.14 0.014 2.56 0.28 0.029 3.587 .000 b R2 2.49 0.27 0.027 2.37 0.24 0.026 3.353 .001 b R3 2.53 0.20 0.020 2.40 0.23 0.024 4.109 .000 c M1 2.67 0.15 0.015 2.57 0.29 0.030 2.907 .004 c M2 2.68 0.29 0.029 2.48 0.25 0.026 5.189 .000 c M3 2.82 0.13 0.013 2.71 0.27 0.028 3.774 .000 d I1 2.58 0.17 0.017 2.48 0.29 0.030 3.044 .003 d I2 2.41 0.18 0.018 2.17 0.22 0.023 8.372 .000 d I3 2.33 0.21 0.021 2.22 0.21 0.022 5.725 .001 T1e 3.33 0.35 0.035 2.99 0.35 0.036 6.683 .000 e T2 3.28 0.41 0.041 3.06 0.40 0.042 3.856 .000 f HB 8.73 0.40 0.040 8.07 0.73 0.077 7.858 .000 g HL 19.12 0.91 0.093 17.82 1.57 0.165 6.937 .000 a L1, L2 and L3: distal, middle and proximal phalange print length of little finger respectively.
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Male
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R1, R2 and R3: distal, middle and proximal phalange print length of ring finger respectively.
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M1, M2 and M3: distal, middle and proximal phalange print length of middle finger respectively.
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I1, I2 and I3: distal, middle and proximal phalange print length of index finger respectively. T1 and T2: distal and proximal phalange print length of the thumb respectively.
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HB: handprint breadth.
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HL: handprint length.
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Significant at p < 0.05.
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All measures in cm.
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Table (2): Correlation coefficient (between stature and handprint measures) and simple linear regression equations for estimation of stature (in cm) from measurements of handprints and phalange prints on male participants.
simple linear regression
S = stature
˟ Correlation is significant at the 0.05 level (2-tailed).
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˟˟ Correlation is significant at the 0.01 level (2-tailed). P < 0.05 is significant.
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R 0.494 0.245 0.134 0.449 0.333 0.031 0.611 0.404 0.387 0.338 0.338 0.529 0.252 0.071 0.422 0.519
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R .244 .06 .018 .202 .111 .001 .375 .163 .150 .114 .114 .280 .064 .005 .178 .269
P .000 .014 .182 .000 .001 .000 .000 .000 .000 .000 .001 .000 .011 .486 .000 .000
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SEE ±5.12 ±5.71 ±5.83 ±5.26 ±5.55 ±5.89 ±4.66 ±5.39 ±5.43 ±5.54 ±5.54 ±4.99 ±5.70 ±5.87 ±5.33 ±4.54
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Equation S= 110.37 + 23.17 L1 S= 155.15 + 6.81 L2 S= 159.77 + 3.92 L3 S= 117.69 + 18.78 R1 S= 149.60 + 7.34 R2 S= 170.01 - 0.84 R3 S= 102.25 + 24.60 M1 S= 145.71 + 8.26 M2 S= 120.13 + 16.93 M3 S= 122.41 +17.63 I1 S= 140.87 + 11.20 I2 S= 134.27 + 14.46 I3 S= 153.77 + 4.24 T1 S= 164.55 + 1.02 T2 S=113.658 + 6.213 S=109.533 + 3.018
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L1 L2 L3 R1 R2 R3 M1 M2 M3 I1 I2 I3 T1 T2 HB HL
Correlation coefficient r .449˟˟ .245˟ .134 .494˟˟ .333˟˟ .029 .612˟˟ .403˟˟ .387˟˟ .503˟˟ .338˟˟ .529˟˟ .252˟ .071 .422˟˟ .618˟˟
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Table (3): Correlation coefficient (between stature and handprint measures) and simple linear regression equations for estimation of stature (in cm) from measurements of handprints and phalange prints on female participants. Simple linear regression R .228 .045 .134 .187 .032 .214 .122 .161 .170 .148 .389 .281 .032 .219 .141 .298
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R .052 .002 .018 .035 .001 .046 .015 .026 .029 .022 .151 .079 .001 .048 .020 .089
p .029 .693 .01 .000 .01 .042 .01 .02 .01 .01 .000 .007 .961 .037 .02 .004
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SEE ± 6.50 ± 6.68 ± 6.62 ± 6.56 ± 6.68 ± 6.52 ± 6.63 ± 6.59 ± 6.58 ± 6.61 ± 6.16 ± 6.41 ± 6.68 ± 6.52 ± 6.61 ± 5.38
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Equation S= 143.346+ 5.836 S= 159.171 -1.249 S= 166.853 -5.134 S= 145.459+ 4.494 S= 156.504+ .193 S= 142.254+ 6.123 S= 149.597+ 2.862 S= 146.333+ 4.283 S= 145.351+ 4.287 S= 148.520+ 3.409 S= 131.247+ 11.832 S= 136.106+ 8.767 S= 156.668+ .098 S= 145.755+ 3.665 S= 146.561+ 1.290 S= 134.516 + 1.259
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L1 L2 L3 R1 R2 R3 M1 M2 M3 I1 I2 I3 T1 T2 HB HL
Correlation coefficient r .229˟ .042 .135 ˟ .188 ˟˟ .051 ˟ .213˟ .124 ˟ .161˟ .171˟ .149 ˟ .389˟˟ .281˟˟ .005 .219˟ .142˟ .412˟˟ S = stature
˟ Correlation is significant at the 0.05 level (2-tailed).
˟˟ Correlation is significant at the 0.01 level (2-tailed).
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P < 0.05 is significant.
Table (4): Stepwise regression equations for estimation of stature (in cm) from handprint measurements in both male and female participants.
female
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Equation S= 88.15 + 8.60 HB + 14.72 I3 -3.331M2 -12.232L2 +37.231 I2 +33.012R2 - 9.385 L1 + 1.626 HL -54.256 R1 + 28.724 M1 -13.928 R3 -6.758 T2 + 19.171 I2 -10.317 L3 S= 79.380 + 7.211 HB + 12.083 I3 -18.432 L2 + 14.076 I2 + 6.464 R2 - 25.184 L1 + 2.270 HL S= 95.632 + 4.976 HB + 8.819 I3 + 8.658 M2 - 8.299 L2 S= 96.595 + 5.876 HB + 8.371 I3 S= 155.221 + 20.513 I1 - 54.551L3 + 4.416 HL - 24.438 HB + 30.154 M3 + 20.488 I3 + 19.631 R1 S= 152.011 + 25.082 I2 - 42.729 L3 + 6.648 HL - 16.455 HB + 17.353 M3 S= 149.068 + 25.246 I2 - 36.324 L3 + 6.676 HL - 11.901 HB S= 145.309 + 12.775 I2 - 35.484 L3 + 2.932 HL
S = stature
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SEE 1.67
R2 .915
3.04
.693
3.76 4.19 1.84
.516 .383 .929
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3.49 4.79
.736 .497
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Fig. 1 Measurements of phalangeal length of handprint using Klonk Image Measurement software. L1, L2 and L3: distal, middle and proximal phalange print length of little finger respectively. R1, R2 and R3: distal, middle and proximal phalange print length of ring finger respectively.
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M1, M2 and M3: distal, middle and proximal phalange print length of middle finger respectively. I1, I2 and I3: distal, middle and proximal phalange print length of index finger respectively.
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T1 and T2: distal and proximal phalange print length of the thumb respectively.
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Fig. 2 Measurements of handprint length and breadth using Klonk Image Measurement software. HL: Handprint length.
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HB: Handprint breadth.
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Highlights Handprint play vital role in both anthropology and crime scene investigation. Scanned hands of both male and female from Egyptian population sample was modified to simulate handprint and different measures were recoded. Using simple and stepwise regression analysis, stature could be predicted from handprint measurements.