Facial soft tissue depths in French adults: Variability, specificity and estimation

Forensic Science International 231 (2013) 411.e1–411.e10

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Forensic Science International journal homepage: www.elsevier.com/locate/forsciint

Forensic Anthropology Population Data

Facial soft tissue depths in French adults: Variability, specificity and estimation Pierre Guyomarc’h a,b,*, Fre´de´ric Santos a, Bruno Dutailly a, He´le`ne Coqueugniot a,c a

Universite´ Bordeaux 1, UMR 5199 PACEA, Anthropologie des Populations Passe´es et Pre´sentes, avenue des Faculte´s, baˆt. B8, F-33405 Talence, France Joint POW/MIA Accounting Command, Central Identification Laboratory, 310 Worchester Avenue, Building 45, Joint Base Pearl Harbor-Hickam, HI 96853, USA c Department of Human Evolution, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, D-04103 Leipzig, Germany b

A R T I C L E I N F O

A B S T R A C T

Article history: Received 19 May 2012 Received in revised form 30 January 2013 Accepted 7 April 2013 Available online 14 May 2013

Facial soft tissue depths (FSTD) are used in facial approximation to render the shape of the face, and are traditionally published specifically to population, corpulence, and sex amongst other factors. This paper investigates the variability of FSTD collected at 37 landmarks on 500 CT (computed tomography) scans of French living individuals. The specificity of the sample is evaluated by comparing values with six published datasets of various populations and recording techniques. Apart from a significant influence of the corpulence, FSTD show negligible variations with age and sex. The differences between the French sample and other datasets contradict the hypothesis of major influence of population, and underline sample specificity linked with technique and methodology of data measurement. Regression equations were computed to estimate FSTD using age, sex, facial build, and craniometrics, leading to more accurate results if such factors are known. Nevertheless, application of the pooled T-table (Tallied-Facial-SoftTissue-Depth-Data) has been validated according to the French sample. ß 2013 Elsevier Ireland Ltd. All rights reserved.

Keywords: Facial approximation Facial reconstruction Soft tissue thickness Craniofacial identification Computed tomography TIVMI

1. Introduction Facial soft tissue depths (FSTD) studies have been conducted and published since the end of the 19th century. They provide biological variability information useful for craniofacial identification techniques as facial approximation and superimposition. FSTD measurement techniques are various and include needle puncture, projectional radiography, ultrasound, magnetic resonance imaging (MRI), and computed tomography (CT). They are performed both on cadavers and living subjects. Since the exhaustive recent reviews by Stephan and Simpson (see [1] for the adult data and [2] for the sub-adult data), several FSTD studies have been published; the need for modern populations data is indeed still emphasised by a majority of the scientific community. Despite active research in France, no wide FSTD database (n > 100) has been compiled for the French population [3–6]. From the first referential published by Welcker in 1883 (FSTD taken at 9 midline landmarks on 13 cadavers with the needle puncture technique) [7], to the TalliedFacial-Soft-Tissue-Depth-Data (T-table) [8], most of the studies

* Corresponding author at: Joint POW/MIA Accounting Command, Central Identification Laboratory, 310 Worchester Avenue, Building 45, Joint Base Pearl Harbor-Hickam, HI 96853, USA. Tel.: +1 808 448 1735; fax: +1 808 448 1982. E-mail addresses: [email protected], [email protected] (P. Guyomarc’h). 0379-0738/$ – see front matter ß 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.forsciint.2013.04.007

strived to provide FSTD databases specific to population, sex, age, and corpulence. Significant differences are indeed observed following such factors. Nevertheless, as stated recently, the subcategorisation by biological factors might not be justified or pertinent [1]; because the FSTD differences between sexes, populations, generations, and measurement techniques, were found to be negligible compared to the study-specific errors, the databases from more than 60 studies were pooled to create the Ttables [1,7–68]. Using a single FSTD table for facial approximation implies that the craniofacial morphology is different enough between individuals to produce unique and specific faces when applied, regardless of the variations induced by the biological factors. On the contrary, following the hypothesis that all details, even slight variations in FSTD, are essential to the process of facial recognition, it can be valuable to apply a set of thicknesses specifically to a target individual. From this perspective, researchers investigated the correlations between FSTD and craniometrics, and published regression formulae allowing for the estimation of the tissue depths [9,54,69]. Similar equations using biological factors (age, sex and body mass index) have also been proposed to increase the specificity in FSTD estimation [70,71]. This paper presents a new referential of FSTD from a French adult modern sample, based on CT-scans of living subjects (n = 500). The variability of this database is explored according to sex, age, and facial build. Six published FSTD tables are

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compared to discuss the population specificity, and regression equations are proposed for the thickness estimation specific to biological factors and craniometrics. 2. Materials and methods 2.1. CT-scan database Computed tomography provides a material increasingly used in the field of facial approximation [6,33,70,72,73]. This technique allows distinguishing soft tissues from bone because they display an important difference in density. CT-scan archives are abundantly available in hospitals; ethical committees (Comite´s de protection des personnes) were consulted [74] and gave their approval for the exploitation of anonymous exams collected in French medical centres (collaborators acknowledged). More than 900 exams were gathered, and a homogeneously distributed sample of 500 CT-scans was established according to sex and age (known for all individuals), after the exclusion of pathological patients. Sex ratio is 1:1.13 (265 males; 235 females), and mean age is 52 years (range = 18–96; standard deviation s = 20). Because medical diagnostic does not necessarily require the acquisition of the whole head, approximately one third of the sample covers the entire face (from glabella to gnathion), and the remaining two thirds depicts only the superior or inferior part of the patient’s face. Moreover, due to the presence of partial exams in the sample, along with the different artefacts inherent to the CT technique (dental metal fillings, life support material, etc.), the FSTD measurements will concern a specific number of subjects for each landmark. The sample is hypothetically representative of the French population because such exam is prescribed in several cases (the true investigated sample is extracted from the population of people CT-scanned in 2008 and 2009 in France). 2.2. TIVMI The CT exams were collected in the DICOM format (Digital Imaging and Communications in Medicine), which consists in a suite of 2D images (i.e., slices) of the subject’s head that include spatial and density information of the anatomical structures. The DICOM files were loaded and processed with TIVMI (Treatment and Increased Vision in Medical Imaging), a software downloadable freely (http:// www.pacea.u-bordeaux1.fr/TIVMI/), and developed by one of the authors (BD). TIVMI performs an accurate and reproducible surface reconstruction method using the Half-Maximum Height algorithm (HMH) [75] in three dimensions (3D) [76]. The uncertainty in measurement related to the surface reconstruction has been evaluated for this technique [77]. The soft and hard parts of the 500 patients were reconstructed using the HMH 3D in TIVMI. The software also proposes numerous geometric operations, including the construction of landmarks, planes, lines and segments in 3D. In order to ease the positioning of cranial landmarks, reference planes were used. The commonly accepted definition of the Frankfurt Horizontal (FH) implies both right and left porions but only the left orbitale, and facial asymmetry may induce asymmetric planes; a mean transverse (MT) plane was thus constructed following the original definition of the FH: a plane that passes through both porions and both orbitales [78]. Since those four landmarks are usually not aligned on the same plane, the original FH was approximated visually or using a craniophore. However, planes are geometrically constructed on three points; in TIVMI, MT is calculated by averaging the perpendicular angles of the planes passing through the four combinations of three of the four landmarks. Subsequent orthogonal construction of the coronal and sagittal planes can be performed, preserving the optimal geometric integrity of the skull orientation. The mean coronal (MC) plane is constructed by averaging a plane orthogonal to MT passing through both porions and another plane orthogonal to MT passing through both orbitales. The mean sagittal (MS) plane is constructed orthogonally to MT and MC, passing through the nasion (Fig. 1). Those three reference planes can also be averaged by pair to construct ‘‘in between’’ planes, that define for example the antero-lateral, antero-superior and antero-inferior orientations of the skull. 2.3. Landmarks and FSTD The landmarks were selected accordingly to the literature on facial soft tissue depths [1,17,79], and taking into account the feasibility of their positioning on CTscan (issues with visibility of thin bone). Geometric landmarks (type II and III [80]) were spotted on the reconstructed skull surface using the reference planes to enhance their repeatability and reproducibility of positioning (e.g., MC was shifted until the most anterior part of the frontal bone is tangent, delimiting the position of the glabella; MS was shifted to find the most lateral part of the zygomatic arch, where the zygion has to be placed, etc.). For a better repeatability of the FSTD measurements, we chose to collect the thicknesses of soft tissues following a constant orientation: a segment was automatically created from the bony landmark, in order to pass through the skin, along the perpendicular angle of a reference plane. The HMH algorithm is then calculated on this segment to locate the exact limit between soft tissues and air. The FSTD recorded is the Euclidean distance between the bony landmark and its homologous cutaneous landmark positioned with the HMH. The tissue thickness is thus recorded according to the main orientation of the skull, and not following the normal vector of the surface of the

Fig. 1. Reference planes: MT (mean transverse), MC (mean coronal), and MS (mean sagittal) depicted on a 3D skull surface reconstructed in TIVMI with the HMH 3D algorithm. bone, which may imply variability within individuals. Our recording technique consequently results in an orientation slightly different from the traditional direction of FSTD measurements; however, the plane chosen for each landmark is consistent with the main orientation traditionally used (Table 1). The three FSTD of the lips are defined by specific bone and skin landmarks, their orientation will thus be variable. Fig. 2 illustrates the position of the landmarks and the FSTD measurement orientations. The uncertainty in measurement was evaluated for the landmark positioning with the dispersion of repeated measures around the mean position (five skulls measured three times by the same observer with one month interval in between each measure, and 20 skulls measured by two observers); the uncertainty reported in linear distances was quantified using the coefficient of variation of the error (CVE) [81] between mean FSTD and repeated measurements (a CVE inferior to 5% indicates reliable data). Table 1 lists the definition of the landmarks used in this study, the plane specifying the orientation of the 37 FSTD measurements, synonyms (when names differ with comparable definitions between studies), and the uncertainty in measurement. 2.4. Estimation of corpulence None of the CT-scan displayed the height and weight of the patients. The body mass index (BMI) is widely used in FSTD studies for sub-classification, along with the factors sex, age, and population. In order to enhance the comparability and the prediction of FSTD with our sample, we decided to estimate the corpulence of the individuals. According to the literature, this factor heavily influences the region of the cheek [15,17,62]. Four landmarks were selected in this region (ectomolare, inframolare, mid-ramus, and gonion), and a Principal Component Analysis (PCA) was performed in Statisticaß (Statsoftß, v. 7.1), on the corresponding FSTD for all the individuals for whom the data was available (n = 220). This first step allowed for the simplification of the information, and for the classification of the subjects in two groups, homologous to a normal corpulence and overweight. Homology with BMI is possible by comparison with a national inquiry on obesity performed in France at the time of the data collection, ObE´pi 2009 [82]. The latter concerns more than 25,000 adult subjects, and states that the mean BMI of the French population is 25.3 kg/m2, that obesity tends to be more present in older individuals, and that men are more overweight than women. Thus, the average of the first principal component (PC1) of the PCA is hypothesised to correspond to an average BMI extracted from the four FSTD selected. A BMI of 25 kg/m2 separates the ‘‘normal’’ group from the overweight group. Following ObE´pi 2009, only 3.6% of the French population is emaciated; a great majority of the subjects are in the normal or overweight group. Because the corpulence is estimated in this study, it is preferable to consider only those two groups to lower classification errors. Moreover, due to the probable presence of truly overweight subjects in the normal estimated BMI category, and vice versa, we use the term ‘‘facial build’’ to signify the global soft tissue pattern. This estimation indicates a trend following the cheek region that is

Table 1 Landmark definition, measurement orientation, uncertainty in measurement (intra- and inter-observer dispersion, CVE = coefficient of variation of the error), and synonyms in previously published studies. Bold type = dispersion superior to 2 mm. Landmark

Definition

Measurement orientation

Uncertainty in measurement Dispersion (mm)

Glabella (g) Nasion (n) Mid-nasal (mn) Rhinion (rhi) Mid-philtrum (mp)

Incisor superius (is)/stomion (sto)

Infradentale (id)/labrale inferius (li)

Labiomentale (labm) Pogonion (pg) Supragnathion (sgn) Gnathion (gn) Frontotemporale (ft) Zygion (zy) Jugale (ju) Frontomalare temporale (fmt) Superciliare (sci) Supraconchion (sk) Ectoconchion (ec)

Orbitale (or) Zygoorbitale (zo) Mid-nasomaxillare (mnm)

Nasomaxillare (nm) Zygomatic (za)

inter-obs

Anterior (MC) Anterior (MC) Anterior (MC)

1.2 0.6 0.8

1.6 0.6 1

<1 <1 1.05

Antero-superior (MC/MT)

0.4

0.7

<1

Anterior (MC)

0.9

1.3

Variable

0.8

1

Variable

1.2

1.6

Variable

0.6

1

Anterior (MC)

0.8

1.2

Anterior (MC)

1

1.1

Antero-inferior (MC/MT)

1.1

1.5

Inferior (MT)

0.9

0.9

Lateral (MS) Lateral (MS)

1.3 1.8

1.8 2.5

2.5 2.04

Lateral (MS)

0.8

1.4

2.57

Lateral (MS)

1

1.3

3.5

Most anterior point of the supraciliary arch in the axe of the centre of the orbit Most superior point of the orbital rim

Anterior (MC)

1.3

2

Anterior (MC)

1.4

2.9

3.08

Lateral point of the orbital rim, opposed to the dakryon (medial point of the fronto-lacrymal suture on the medial orbital rim) Most inferior point of the orbital margin Point of the zygo-maxillary suture on the orbital rim

Anterior (MC)

2.1

3.2

5.03

Anterior (MC) Anterior (MC)

1.1 0.9

2 1.8

Lateral (MS)

1.5

1.8

1.45

Inferior eye orbit [39] Mid-infraorbital [1]; sub-orbital [17,72]; infra orbital [15] Lateral nasal [72]

Lateral (MS)

1.3

1.7

2.2

Lateral nasal [17]

Antero-lateral (MS/MC)

2.1

5.2

3.23

Antero-lateral (MS/MC) Antero-lateral (MS/MC)

1.5 2.7

1.9 6.1

<1 1.77

Most anterior midline point on the frontal bone Midline point on the naso-frontal suture Midline point on the internasal suture midway between nasion and rhinion Midline point at the inferior free end ot the internasal suture Midline point midway between the base of the nasal spine and prosthion on the anterior edge of the maxillae Midline point at the most anterior edge of the superior alveolar ridge of the maxillae (STD measured with the midline point of the upper lip) Midline point on the tangent of the inferior borders of the central superior incisives (STD measured with the midline point at the junction of the upper and lower lips) Midline point at the most anterior edge on the inferior alveolar rodge ot the mandible (STD measured with the midline point of the lower lip) Deepest midline point in the groove superior to the mental eminence Most anterior midline point on the mental eminence of the mandible Most antero-inferior midline point of the mental eminence between pogonion and gnathion Most inferior midline point at the mental symphysis of the mandible Most anterio-medial point of the linea temporalis superior Most lateral extent of the lateral surface of the zygomatic arch Most antero-inferior point on the posterior border of the zygomatic bone Most posterior point of the zygo-frontal suture

Point of the naso-maxillary suture between the nasomaxillare and the nasomaxillofrontale (junction of the frontal, maxillar and nasal bones) Most inferior point of the naxo-maxillary suture on the nasal aperture Most salient point of the zygomatic on the zygomatic major muscle insertion Most inferior point on the zygo-maxillary suture Most postero-medial point of the maxilla in the supra canine fossa

1.89 <1

2.09

<1

1.4 <1

End of nasals [17,39,72] Subspinale [72]; point A [6] Mid upper lip margin [72]; upper lip [17] Upper incisor [72]

Mid lower lip margin [72]; lower lip [17] Mentolabial sulcus [1]; supra-mentale [15,72]; chin-lip fold [17]; point B [6] Mental eminence [17,39,72]

1.46 <1

<1

<1 <1

Menton [1]; beneath chin [17,39,72]

Lateral zygomatic arch [72]; zygomatic arch [17]; root of zygoma [39] Lateral orbit [17] Mid lateral orbit [17]; lateral eye orbit [39] Supraorbital [15,17]; superior eye orbit [39] Mid-supraorbital [1]; supra-orbital [72]; orbitale superius [6]

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Zygomaxillare (zm) Canine fossa (cf)

CVE (%)

intra-obs

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Prosthion (pr)/labrale superius (ls)

Synonyms

2.75 5

Mid mandible [39]

<1 4.22 0.8 3.9

Middle mandibular [15]

<1 1.5

Infra M2 [1]; sub M2 [17,72]; lower cheek [39] Sub canine [39] <1 1.4

Lateral supra-labiale [72] <1 1.5

1.15 3.5

<1

<1 1.58 1.9 2

1.1

4.18 inter-obs

3.7

Synonyms CVE (%)

0.9

1.1

1.2

1.2

0.6 2.1

3.2

Anterior (MC)

Lateral (MS)

Anterior (MC)

Antero-lateral (MS/MC) Lateral (MS)

Lateral (MS)

2.7 Lateral (MS)

Lateral (MS)

1.4 1.7 Lateral (MS) Lateral (MS)

Point on superior alveolar ridge superior to the crown of the maxillary second molar Point on superior alveolar ridge superior to the crown of the maxillary canine Point on inferior alveolar ridge inferior to the crown of the mandibular second molar Point on inferior alveolar ridge inferior to the crown of the mandibular canine Most anterior point of the mental foramen Point on the inferior border of the corpus of the mandible midway between gonion and pogonion Point on the centre of the corpus of the mandible located on the oblique line

1.6 Lateral (MS)

Most supero-medial point on the maxillary inflexion between the zygomaxillare and the ectomolare Most lateral point of the glenoid process of the mandible Point on the lateral border of mandiblular angle where a tangent bisects the angle formed by the posterior ramus and the inferior corpus borders Point at the centre of the mandibular ramus

intra-obs

Measurement orientation

Dispersion (mm)

homologous to the BMI, even if the two categories do not correspond strictly to each other. Once the facial build of the first 220 individuals is estimated, the available FSTD of the remaining subjects are compared in terms of correlation with the FSTD at the ectomolare, inframolare, mid-ramus, and gonion. Using the most correlated FSTD, it was possible to determine the most probable build group of the individuals with Discriminant Function Analysis. Validation of this estimation was verified through comparison of demographic trends between ObE´pi 2009 and the study’s sample. 2.5. Asymmetry, influence of sex, age, and corpulence The FSTD were statistically explored in Statisticaß, first in order to check if the variables followed a normal distribution (through the Shapiro–Wilk test). Significant difference between right and left FSTD, or asymmetry, was then tested with a paired samples t-test (or a Wilcoxon signed-rank test as an alternative in case of non-normal distribution). Because sex, age, and corpulence are not necessarily independent in their influence on FSTD, a multifactorial analysis of variance (MANOVA) was performed on the measurements. This test allows for the evaluation of the independent effects and the interactions between factors. Only the level of significance (p) was presented in the results (significant threshold after a Bonferroni adjustment at p < 0.001). A weighted mean difference (WMD) was then calculated, by summing the square root of the squared differences, divided by the number of FSTD compared. The WMD, in millimetres, indicates the global distance between two sets of measurements. 2.6. Comparative databases

Definition

Uncertainty in measurement

Mid-masseteric [72]; mid masseter [17]; occlusal line [15] Supra M2 [1,15,17,39,72]

P. Guyomarc’h et al. / Forensic Science International 231 (2013) 411.e1–411.e10

Supraglenoid [17]

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Six published databases have been selected for comparison. Exhaustive description of the methodology and precise definition of the landmarks were the first criteria for this selection. Table 2 presents the characteristics of the chosen recent studies. They cover different populations and techniques. Needle puncture and ultrasound techniques imply slight differences between landmark definitions because the measurement is based on palpation (the bone is not visible when FSTD are recorded). Even if the studies of Tilotta et al. [6] and Cavanagh and Steyn [72] used CT scans, their different methodologies also imply variations in measurement. While the first one [6] is similar to our protocol (positioning of landmarks on a 3D reconstructed surface of the skull), the second one [72] concerns measurements directly performed on the 2D DICOM slices. Both of those CT studies used the perpendicular angle of the bone, and our methodology used the reference planes to determine the measurement orientation. Moreover, the comparability between measurements may be jeopardised as each observer may have a slightly different appreciation of the same landmark. The weighted T-table [8], however, is likely to be free from variations between studies, as it is built on several datasets of different populations and techniques [1]. Because our study only uses two groups for the corpulence factor (normal or strong build), the weighted mean of the emaciated and normal categories is calculated for the publications using three groups. For each comparative database, a specific number of FSTD are compared; the WMD was calculated in order to evaluate the global difference between our study and the compared data. Finally, the correlation between the global patterns of the compared databases (through the Pearson’s product-moment correlation coefficient, r) indicated whether the differences are proportional, or if there really exists significant variations among populations or measurement techniques. 2.7. Estimation of soft tissue depths Applying a dataset of mean FSTD, specific or not to sex, corpulence, and population, may be simplistic. It appears more valuable to propose an estimation of tissue thickness, specifically to the individual: recent researches used biological factors [71] and craniometrics [54,69] for that purpose. Following these studies, new regression equations were proposed using both craniometrics and biological factors according to the results of the influence of the factors on the FSTD, and correlations with craniometric variables. Several regression models were explored to produce the best estimation formulae according to the Akaike information criterion (measure of the relative goodness of fit in a statistical model) [83]. The craniometric variables used in this study are presented in Table 3.

Mid-mandibular body (mmc)

Mental foramen (fm) Midmandibular border (mmb)

Infra canine (ic)

Inframolare (im)

Supra canine (sc)

Ectomolare (ecm)

Mid-ramus (mr)

Condylion (co) Gonion (go)

Submaxillar curvature (smc)

Landmark

Table 1 (Continued )

3. Results 3.1. Preliminary analyses The CVEs indicate that the impact of the uncertainty in measurement is negligible (below 5%) on the 37 FSTD (Table 1). Only 6 of the 37 FSTD follow a normal distribution (supraconchion, supraciliare, prosthion/labrale superius, incisor superius/stomion, pogonion and supragnation). Alternative non-parametric tests are used for the other variables in the following results. None of the 25 bilateral FSTD revealed a statistically significant asymmetry. A

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Fig. 2. Position of the cranial landmarks at which soft tissue depth has been recorded. Measurement orientation is displayed with arrows (see Table 1 for landmark definition).

mean between the right and left measures is used for the following analyses. The estimated corpulence was achieved through PC1 of the PCA for the 220 individuals displaying FSTD on the four target cheek landmarks. The highest correlation between PC1 and the other FSTD was found at the zygion (r = 0.79), and allowed for the determination of the corpulence group for 257 individuals. The remaining 23 subjects were assigned to groups using lower correlated FSTD (ectomolare, zygomaxillare, gnathion, and labiomentale). The proportion of normal individuals (BMI < 25) vs. overweight subjects (BMI > 25) is balanced (n = 253 and 247, respectively).

The age repartition according to the corpulence factor in the French sample is directly compatible with the French population repartition stated by ObE´pi 2009; there is a statistically significant increase of the BMI with age (Pearson’s Chi square = 19.8; p < 0.01; test performed on the demographic age intervals used in ObE´pi). Also in coherence with the trends observed through ObE´pi, the study’s sample shows a significant difference in BMI between males and females, the former group being more corpulent than the latter (Pearson’s Chi square = 8.3; p < 0.01). Those similarities consolidate the representativeness of the sample towards the French population.

Table 2 Comparative studies selected, number of individuals (n), population, technique used, and age ranged. Publication

n

Population

Technique

Age (years)

(T-table) Stephan [8] Cavanagh and Steyn [72] Tilotta et al. [6] Manhein et al. [39] Codinha [15] De Greef et al. [17]

545–7382 154 47 197 151 967

Pooled sample South Africa/Black females France/females America/Black and White Portugal Belgium

Various CT CT Ultrasound Needle Ultrasound

>18 18–35 20–40 19–97 20–99 >18

Table 3 Cranial measurement definition, mean, number of individuals (n), and code (according to [88]) for the French sample. Variable

Definition

Landmarks

Mean

n

Code [88]

WFB UFBR FMB ZYB JUB ZMB FHN NPH ALV OBB OBH NLH HTN HTN2 HAP LON LMA LMD CBD GOG LCM GNI HDT

Least frontal breadth Outer biorbital breadth Inner biorbital breadth Bizygomatic breadth Bijugal breadth Bimaxillary breadth Total facial height Nasoalveolar height Nasospinale-prosthion height Orbital breadth Orbital height Nasal height Total nasal height Piriform aperture height Piriform aperture breadth Nasal border length External palate breadth Alveomandibular breadth Bicondylar breadth Bigonial breadth Length of the mandibular body Height of mandibular symphysis Dental height

Right and left frontotemporale Right and left frontomalare temporale Right and left frontomalare orbitale Right and left zygion Right and left jugale Right and left zygomaxillare Nasion/gnathion Nasion/prosthion Nasospinale/prosthion Maxillofrontale/ectoconchion Orbitale/supraconchion (projected) Nasion/nasospinale Nasion/akanthion Nasion/subspinale Rhinion/nasospinale Nasomaxillofrontale/nasomaxillare Right and left ectomolare Right and left inframolare Right and left condylion Right and left gonion Left gonion/pogonion Gnathion/infradentale Prosthion/infradentale

96.0 100.4 97.4 127.8 110.3 89.4 119.1 67.7 18.3 38.7 35.6 50.0 52.4 55.7 34.1 24.9 58.4 61.4 119.9 94.2 86.0 28.9 23.9

370 256 380 380 255 363 249 330 321 375 375 452 446 442 452 358 307 261 247 278 261 258 275

M9 M43 M43(1) M45 M45(1) M46b M47 M48 M48(1) M51 M52 M55 M55a

M56(2) M61 M65 M66 M69

411.e6 Table 4 Number of individuals for which facial soft tissue depth was measured (n), mean, minimum, maximum, standard deviation (s), and results for the MANOVA analyses: individual and interaction effects for sex (male vs. female), age (<39 years vs. >40 years), and build (estimated BMI < 25 kg/m2 vs. BMI > 25 kg/m2). Bold type = significant at p < 0.001. Soft tissue depth

General data n

Mean

Multifactorial MANOVA results Min

Max

s

Sex

Age

(mm) Midline

Frontotemporale (ft) Zygion (zy) Jugale (ju) Frontomalare temporale (fmt) Superciliare (sci) Supraconchion (sk) Ectoconchion (ec) Orbitale (or) Zygoorbitale (zo) Mid-nasomaxillare (mnm) Nasomaxillare (nm) Zygomatic (za) Zygomaxillare (zm) Canine Fossa (cf) Submaxillar curvature (smc) Condylion (co) Gonion (go) Mid-ramus (mr) Ectomolare (ecm) Supra canine (sc) Inframolare (im) Infra canine (ic) Mental foramen (fm) Midmandibular border (mmb) Mid-mandibular body (mmc)

Sex  Age

Sex  Build

Age  Build

Sex  Age  Build

366 469 321 459 354 294

6.5 8.2 5.5 3.0 12.9 14.1

3.3 4.8 1.1 0.8 6.7 7.7

10.6 14.5 9.5 7.6 31.2 23.1

1.2 1.6 1.3 0.9 2.8 2.5

0.02 <0.001 <0.001 <0.001 <0.001 <0.001

0.01 0.52 0.74 <0.001 0.01 0.29

<0.001 <0.001 <0.001 <0.001 0.02 0.07

0.54 0.87 0.31 0.24 0.54 0.02

0.25 0.36 0.50 0.72 0.30 0.55

0.96 0.02 0.25 0.52 0.83 0.31

0.93 0.70 0.21 0.12 0.31 0.52

203

7.7

1.4

17.1

2.7

<0.001

0.00

0.83

0.87

0.47

0.05

0.66

266 253 254 234 211

16.8 12.7 11.8 9.4 9.5

9.7 7.9 5.7 3.4 4.0

29.8 22.2 18.4 14.8 22.9

3.1 2.3 2.1 2.1 3.3

0.03 0.04 <0.001 0.01 0.03

0.25 <0.001 0.86 0.60 0.45

0.3 0.01 <0.001 <0.001 <0.001

0.35 0.67 0.95 0.32 0.95

0.62 0.68 0.46 0.54 0.75

0.21 0.54 0.03 0.07 0.64

0.46 0.97 0.51 0.81 0.53

361 364 245 242 367 373 372 371 371 328 457 369 353 440 330 215 238 327 300 307 246 262 255 242 246

6.7 10.0 10.9 9.1 8.0 9.7 7.8 8.6 7.2 3.5 4.9 10.0 15.1 14.9 28.2 16.7 18.5 22.7 34.5 13.4 27.7 12.8 12.8 14.6 18.2

3.1 3.8 5.9 4.4 4.2 5.2 2.6 2.2 2.7 0.9 1.9 5.3 3.5 8.4 20.0 8.2 5.5 13.4 23.0 7.0 5.5 6.9 8.4 5.2 9.2

16.9 19.5 18.7 17.0 13.2 15.3 16.1 16.9 15.6 7.9 10.8 16.5 23.6 28.3 40.6 29.1 39.1 44.0 48.4 27.0 42.9 20.5 20.3 29.8 34.3

1.7 2.8 2.2 2.1 1.4 1.8 2.6 2.3 2.3 1.3 1.5 2.1 2.5 3.2 3.9 4.1 6.9 4.8 5.0 2.6 5.3 2.1 2.1 4.3 4.4

<0.001 0.25 <0.001 <0.001 <0.001 <0.001 0.15 0.38 0.63 0.08 0.01 <0.001 0.86 0.94 0.49 0.04 0.93 <0.001 0.01 <0.001 0.43 0.01 <0.001 0.51 0.44

0.13 0.34 0.48 0.04 0.01 0.02 <0.001 0.01 0.09 0.02 0.01 0.50 0.29 0.63 <0.001 0.49 0.40 0.45 <0.001 <0.001 0.53 0.62 0.05 0.54 0.48

<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.01 <0.001 <0.001 <0.001

0.37 0.25 0.59 0.43 0.42 0.33 0.70 0.36 0.22 0.84 0.07 0.51 0.87 0.40 0.87 0.03 0.78 0.07 0.68 0.10 0.60 1.00 0.61 0.04 0.25

0.61 0.91 0.70 0.58 0.71 0.56 0.32 0.88 0.13 0.66 0.38 0.70 0.74 0.71 0.77 0.69 0.60 0.09 0.05 0.66 0.25 0.48 0.40 0.25 0.49

0.47 0.16 0.80 0.05 0.34 0.44 0.61 0.58 0.34 0.12 0.3 0.42 0.59 0.42 0.86 0.77 0.65 0.75 0.36 0.91 0.53 0.72 0.08 0.05 0.25

0.77 0.59 0.55 0.68 0.84 0.26 0.64 0.71 0.89 0.43 0.36 0.97 0.70 0.54 0.70 0.18 0.76 0.05 0.35 0.77 0.32 0.85 0.53 0.71 0.82

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Bilateral

Glabella (g) Nasion (n) Mid-nasal (mn) Rhinion (rhi) Mid-philtrum (mp) Prosthion (pr)/labrale superius (ls) Incisor superius (is)/stomion (sto) Infradentale (id)/labrale inferius (li) Labiomentale (labm) Pogonion (pg) Supragnathion (sgn) Gnathion (gn)

Build (p)

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Table 5 Weighted mean differences (WMD in mm) and correlation (Pearson’s coefficient, r) between the French sample and the six studies from Table 2. When possible, subsample comparisons according to sex (F = female; M = male), population (Pop; B = Black; W = White), age (in years), and body mass index (BMI, homologous to the estimated build) were computed. STD dataset

Sex

Pop

Age

T-table Stephan [8] Cavanagh and Steyn [72] Tilotta et al. [6]

All F F

– B W

All 18–35 20–40

Manhein et al. [39]

F M F M F F M F M F M F M

B

W

M Codinha [15]

W F

M

De Greef et al. [17]

W

F

19–34 19–34 35–45 35–45 46–55 19–34 19–34 35–45 35–45 46–55 46–55 56 56 26–91 26–91 20–99 20–99 18–39 30–39 40–49 50–59 60 18–39 30–39 40–49 50–59 60 18–39 30–39 40–49 50–59 60 18–39 30–39 40–49 50–59 60

3.2. Variability The mean FSTD recorded at the 37 landmarks are presented in Table 4. A MANOVA was performed to evaluate the influence of sex (male vs. female), age (below or above 40 years), and corpulence (build with an estimated BMI below or above 25 kg/m2), the p-level for this analysis is also presented in Table 4 (a Bonferroni adjustment was applied for 37 tests, the significant decision threshold is then brought to p > 0.001). The influence of age is relatively scarce, and does not reveal any specific pattern. The weighted mean difference between the means of individuals below 40 years and subjects older than 40 years is low (WMDage = 0.9 mm). Applying a FSTD dataset specific to age will result in negligible amelioration according to the French sample. The influence of sex is more important and concentrated on the superior and lateral orbital region, the nasal bridge, and the antero-superior alveolar process. Even if significantly different between males and females, the WMDsex only equals 1.2 mm. The corpulence tends to heavily influence the majority of FSTD (except the midline

BMI

WMD

All

<25 kg/m2

<25 kg/m2 >25 kg/m2 <25 kg/m2 >25 kg/m2

<25 kg/m2

>25 kg/m2

<25 kg/m2

>25 kg/m2

r 0.94 0.43 0.69

0.97 0.97 0.93

0.68 0.70 0.51 0.79 0.66 0.64 0.71 0.72 0.84 0.69 0.99 0.62 0.96

0.73

0.95 0.91 0.96 0.91 0.92 0.96 0.93 0.95 0.89 0.92 0.88 0.95 0.92

1.12 1.26 1.22 1.17

1.19

0.90 0.93 0.88 0.92

0.74 0.83 0.75 0.93 0.68 1.09 0.98 0.93 1.09 1.03 0.67 0.61 0.70 0.59 0.62 0.94 0.91 0.88 0.48 0.85

0.82

0.95 0.95 0.94 0.95 0.95 0.96 0.97 0.96 0.95 0.95 0.95 0.95 0.95 0.95 0.96 0.94 0.93 0.95 0.93 0.97

region of the mouth), and shows a higher mean difference between normal subjects and subjects with a stronger build (WMDBuild = 2.4 mm). Almost no interaction effect was detected; this means that influences of sex, age and build are globally independent on the FSTD. Even if older and masculine individuals tend to have higher BMI according to both ObE´pi 2009 and the estimated build of the French sample distribution, this general trend is not important enough to be statistically significant on the 37 tested FSTD. 3.3. Specificity The potential uniqueness of the French FSTD referential was evaluated by comparison with six studies (Table 2). The differences between datasets is globally low (Table 5); the most important weighted mean difference is 1.2 mm with the Portuguese database [15], and the lower difference with the French sample is observed with the South African dataset [72]. The correlations between subsamples range from 0.88 to 0.97, which confirms their strong similarity with the French sample.

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Table 6 Regression equations for the estimation of facial soft tissue depths using build (normal = 0; strong = 1), age (age < 40 = 0; age > 40 = 1), sex (female = 0; male = 1), and craniometrics (see Table 3 for measurement definitions). Coefficient of determination (r2), standard error of the estimate (SEE) and sample size (n) are also presented. Soft tissue depth

Formula

r2

SEE

n

Midline

Glabella (g) Nasion (n) Mid-nasal (mn) Rhinion (rhi) Mid-philtrum (mp) Prosthion (pr)/labrale superius (ls) Incisor superius (is)/stomion (sto) Infradentale (id)/labrale inferius (li) Labiomentale (labm) Pogonion (pg) Supragnathion (sgn) Gnathion (gn)

0.83  Build + 0.32  Age + 0.26  Sex + 5.67 0.88  Build + 1.42  Sex + 7.07 0.54  Build + 1.16  Sex + 4.61 0.04  ZMB + 0.34  Build + 0.01  Sex 0.73 0.13  FMB 0.08  WFB 0.87  Age + 1.82  Sex + 6.83 0.83  Build 0.13  NPH + 2.11  Sex + 21.63 1.48  Sex 1.44  Age + 7.73 0.28  HDT + 0.97  Build 0.94  Age + 10.29 0.05  FHN + 0.86  Build + 1.23  Age + 5.03 1.31  Build + 1.05  Sex + 10.63 1.26  Build + 0.85  Sex + 8.31 2.27  Build + 1.47  Sex + 7.64

0.17 0.32 0.25 0.11 0.31 0.19 0.13 0.28 0.20 0.19 0.16 0.19

1.1 1.3 1.2 0.9 1.9 2.2 2.5 2.7 2.1 1.9 1.9 3.0

366 469 321 354 224 283 203 226 241 254 234 211

Bilateral

Frontotemporale (ft) Zygion (zy) Jugale (ju) Frontomalare temporale (fmt) Superciliare (sci) Supraconchion (sk) Ectoconchion (ec) Orbitale (or) Zygoorbitale (zo) Mid-nasomaxillare (mnm) Nasomaxillare (nm) Zygomatic (za) Zygomaxillare (zm) Canine Fossa (cf) Submaxillar curvature (smc) Condylion (co) Gonion (go) Mid-ramus (mr) Ectomolare (ecm) Supra canine (sc) Inframolare (im) Infra canine (ic) Mental foramen (fm) Midmandibular border (mmb) Mid-mandibular body (mmc)

0.09  LMA + 0.16  UFBR 0.18  WFB + 1.51  Build + 2.18 3.88  Build + 7.93 0.04  ZYB 0.13  JUB + 2.32  Build 0.92  Sex + 9.75 0.12  LMA + 0.24  FMB 0.35  UFBR + 1.69  Build + 1.12  Sex + 12.33 0.06  LMA + 1.38  Build + 0.41  Age + 0.47  Sex + 3.20 1.28  Build + 0.44  Age + 1.11  Sex + 8.14 1.67  Build + 1.14  Age + 6.16 1.68  Build + 0.63  Age + 7.26 1.74  Build + 6.27 0.11  OBH 0.11  HAP 0.59  Age + 3.49 0.17  LON 0.05  HAP 0.48  Age + 2.62 1.98  Build 1.73  Sex + 9.87 0.08  LMA + 2.89  Build + 9.28 2.29  Build + 13.77 0.09  GOG 0.17  HTN + 4.24  Build + 1.19  Age + 25.26 4.23  Build 0.18  CBD + 1.40  Age + 2.86  Sex + 33.92 0.14  FHN 0.41  GOG + 8.17  Build + 1.92  Sex + 35.38 0.14  LMA + 5.91  Build + 1.81  Sex + 10.81 0.36  ZYB 0.37  LMA 0.14  NPH + 5.05  Build + 16.91 1.09  Build 1.27  Age + 2.17  Sex + 12.48 0.35  LMA 0.31  LMD + 6.89  Build + 23.39 0.90  Build + 0.80  Sex + 11.93 0.07  UFBR + 1.48  Build 0.86  Age + 1.97  Sex + 4.35 0.15  ZYB 0.19  LCM + 4.28  Build + 9.99 0.13  GOG 0.20  HDT 0.13  LCM + 5.38  Build + 1.23  Age + 18.57

0.39 0.49 0.31 0.42 0.31 0.26 0.16 0.16 0.15 0.11 0.13 0.37 0.32 0.13 0.40 0.44 0.47 0.50 0.61 0.25 0.48 0.10 0.44 0.35 0.52

1.5 1.9 1.8 1.6 1.3 1.6 2.4 2.1 2.1 1.3 1.2 1.6 2.1 2.9 3.2 3.1 4.9 3.4 3.1 2.3 3.9 2.0 1.6 3.5 3.1

144 364 244 147 176 373 372 371 371 232 342 369 181 440 234 215 217 279 172 307 246 262 130 124 242

The WMD of Table 5 were compared using Mann–Whitney Utests according to sex, build, and population, and Spearman correlation for age classes. There was a significant difference between the sex-specific datasets (mean WMD for female = 0.73; for male = 0.91; Z = 3.21; p = 0.001), and more differences for strongly built individuals (mean WMD for normal build = 0.78; for strong build = 0.97; Z = 2.19; p = 0.03). No significant differences in WMD were found between populations (mean WMD for Black = 0.67; for White = 0.77; Z = 1.17; p = 0.24), or age groups (r = 0.07; p < 0.05). As male and ‘‘overweight’’ subjects tend to present thicker FSTD, the specificity of sex- and build-categorised datasets is more important. Nevertheless, all mean WMD are below 1 mm. 3.4. Estimation Following the methodology of three previous studies [54,69,71], specific regression equations were computed using biological factors (sex, age, and build), along with craniometric measurements. The equations for the 37 FSTD are presented in Table 6. Different specific datasets for the French sample can be computed using the mean variables of Table 3 if needed. As categorical factors are used (strong build = 1), the mean value of 0.5 can be used for unknown factors. The standard error of the estimate (SEE) is systematically inferior to the standard deviation of the mean of the French sample: using the formulae thus leads to a higher accuracy. Differences range from inferior to 0.1 mm (for rhinion and mid-nasomaxillare) to 2 mm (for gonion). The

determination coefficients (r2) range from 0.10 (for infra canine) to 0.61 (for ectomolare). 4. Discussion The variability of the French FSTD sample of this study is comparable to what has been previously described in the literature, with thicker values for males, overweight subjects, and older individuals. The mean differences in FSTD between groups are relatively low for age and sex, even if statistically significant results were found. In practice, this confirms that applying specific datasets for male or female will be negligible in terms of rendering for a facial approximation [84]. Corpulence (i.e., estimated build in this study) is the only factor that may have an important impact, but prior knowledge of such factor in forensic anthropology is relatively rare. Moreover, our results suggest that the specificity of the French FSTD dataset is low: differences between published datasets and the study’s sample are lower, or equivalent to the differences between males and females. Overall, our findings indicate that the use of a single weighted FSTD dataset, as the T-table [8], is a valid method. The young females of our sample are closer in FSTD to Black females from South Africa [72] than French females [6] (Table 5). Even if those 3 samples are similar in age, sex, and technique (CT scan), the methodology used to collect the data differs (i.e., 2D vs. 3D, measurement orientation following planes vs. perpendicular to the bone surface). Also, differences detected with the ultrasound-based data [17,39] may be due to the subject’s position (i.e., upright for ultrasound, supine

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for CT), especially in the cheek region, and for overweight and/or older individuals. When FSTD samples are compared, the specificity observed is likely to be related to the conjunction of several factors, of which the most weighted appears to be the technique/methodology of data collection [1]. Population specificity in soft tissue depth data may exist, but what is truly observed is a sample specificity due to cumulated variations related to several factors (including population and measurement methodology). When applied, a set of FSTD will eventually produce a face specific to the skull morphology; specific differences between populations on cranial morphology will be directly reported on the reconstructed soft tissues. The problem of population specificity in facial shape approximation may be eventually less important than previously emphasised. Facial recognition is a complex task which specific processes are not yet fully grasped [85,86]. The amount of precision in facial approximation, needed to favour recognition, is consequently not known. In practice, it is thus interesting to produce the most accurate approximation that will lead to the best results. Estimation of FSTD following recent publications (e.g., [54,69,71]) will avoid important errors. If biological factors are known, the application of the equations in Table 6 can give slightly better precision than the simple application of means. Automatic calculation of FSTD with those equations are integrated in a new computerised method, AFA3D (Anthropological Facial Approximation in 3 Dimensions) [74]. Both approaches (application of the T-table and estimation of specific FSTD) can be used, whether biological factors (especially corpulence) are known or not, and they may both lead to similar facial rendering. Choice is left to the practitioner’s experience, depending on the technique preferred: the slight different FSTD will appear negligible in manual techniques, but may be more pertinent in computer-assisted methods. The visual impact of those different methodologies will need to be investigated on an independent sample. Production of several approximations for the same skull following specific age and/or sex [87] may nevertheless still be useful if the expert applies textures (as facial hair or makeup). Even if shape changes are negligible between young and old subjects, suggestion of wrinkles and loss of skin elasticity might help facial recognition. 5. Conclusion The analysis of soft tissue depths in 500 modern French individuals indicated a significant variability according to the corpulence, but weak differences between males and females, or young and old individuals. The specificity of this sample is relatively low since it presents weighted mean differences with other published samples globally below 1 mm, with strong correlations between the general patterns of the datasets. Such results validate the utilisation of a single referential regardless of age, sex, and population (as the T-table [8]). It is also possible to determine individual-specific FSTD with the estimation formulae using biological factors (age, sex, corpulence, and craniometrics). Application of FSTD is important in facial approximation, but it only represents the first step. A major aspect to help facial recognition is the estimation of the shape and position of the facial sensory organs (eyes, nose, mouth, and ears). Once the form of the face and its features are estimated, textures and details including skin colour, effects of age, and hair, will have to be added to the facial approximation to provide a realistic rendering of the face and lead to recognition. However, we emphasise that only moderate artistic features should be used to ‘‘humanise’’ the approximation, as strong elements (e.g., accentuated make-up, extensive facial hair, etc.) may mask the estimated morphology and compromise the potential recognition.

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Acknowledgements The authors wish to acknowledge the healthcare professionals who allowed and helped for the CT-scan database collect: Pr. Christophe Aube´ and Dr. Jean-Yves Tanguy (CHU Angers); Pr. Christophe Cognard, Serge Martinez and Corinne Viard (Hoˆpital Purpan, Toulouse); Dr. Paul Ardilouze, Dr. David Higue´ and Dr. Charles Laurent (CHCB, Bayonne); Dr. Jack Richecoeur (CH Rene´ Dubos, Pontoise); Dr. Raphae¨l Legghe (Polyclinique du Bois, Lille); Pr. Jean-Nicolas Dacher and Dr. Emmanuel Gerardin (CHU Rouen); Dr. Anne-Sophie Ricard and Pr.Vincent Dousset (CHU Pellegrin Tripode, Bordeaux); Pr. Michel Montaudon (Hoˆpital Haut-Le´veˆque, Pessac); Dr. Jean-Paul Delhaye (CH Pierre Ourdot, BourgoinJallieu). Many thanks go to Le´onie Rey (Universite´ Bordeaux 1) for the repeated measurements she performed. Thanks also to www.CRANIOFACIALidentification.com for making the ‘‘18 years and over T-table available’’. Financial support for this work has been provided by a Ph.D. scholarship granted by the French Ministry of Research (Ministe`re de l’Enseignement Supe´rieur et de la Recherche), and by the research project ‘‘Reconstitution faciale par imagerie 3D’’ granted by Bordeaux 1 University (head He´le`ne Coqueugniot).

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