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Reliance on primary identifiers (DNA, fingerprints) alone, can delay or hamper forensic human identification. At the same time, the use of techniques with unknown accuracy can compromise identification efforts. Craniofacial Superimposition has faced scrutiny due to limitations in reliability studies, such as small samples, non-representative case selections and unrealistic experimental setups. These studies often lacked comprehensive cross-comparisons and disregarded recommended practices. Building upon insights from a previous validation study, which highlighted limitations caused by material quality and quantity, a blind validation study focusing on a contemporary sample with superior photographic quality has been conducted. Ten practitioners with different levels of expertise addressed the identification scenario under blind conditions employing a novel AI-guided Craniofacial Superimposition technology, which automates tasks within the application of the technique. Two conclusions are reached: (i) the combination of human expertise and AI assistance can lead to reliable identification outcomes. In particular, it is noteworthy that experienced participants achieved a 100 % correct decision rate, suggesting that prior experience and expertise in the technique contributes to improved decision-making ability, even when aided by AI tools; (ii) the utilization of an automatic AI-based ranking tool significantly reduced the workload for participants by providing a prioritized list of potential matches, narrowing down the number of comparisons from 1175 to 141. Additional constraints and sources of error have been identified. The study was conducted with Skeleton‑ID v4.2, a commercial AI tool (Panacea Cooperative Research, Spain); authors’ competing interests are disclosed in full.

In 2017, a series of human remains corresponding to the executed leaders of the “January Uprising” of 1863–1864 were uncovered at the Upper Castle of Vilnius (Lithuania). During the archeological excavations, 14 inhumation pits with the human remains of 21 individuals were found at the site. The subsequent identification process was carried out, including the analysis and cross-comparison of post-mortem data obtained in situ and in the lab with ante-mortem data obtained from historical archives. In parallel, three anthropologists with diverse backgrounds in craniofacial identification and two students without previous experience attempted to identify 11 of these 21 individuals using the craniofacial superimposition technique. To do this, the five participants had access to 18 3D scanned skulls and 14 photographs of 11 different candidates. The participants faced a cross-comparison problem involving 252 skull-face overlay scenarios. The methodology follows the main agreements of the European project MEPROCS and uses the software Skeleton-ID™. Based on MEPROCS standard, a final decision was provided within a scale, assigning a value in terms of strong, moderate, or limited support to the claim that the skull and the facial image belonged (or not) to the same person for each case. The problem of binary classification, positive/negative, with an identification rate for each participant was revealed. The results obtained in this study make the authors think that both the quality of the materials used and the previous experience of the analyst play a fundamental role when reaching conclusions using the CFS technique.

•Facial soft tissue thickness are a cornerstone of craniofacial identification.•Many methods have been used to measure facial soft tissue thicknesses.•Measurement errors appear to be large and have not been sufficiently documented.•We review pre-existing data and recommend new future standards. Facial soft tissue thicknesses (FSTT) form a key component of craniofacial identification methods, but as for any data, embedded measurement errors are highly pertinent. These in part dictate the effective resolution of the measurements. As herein reviewed, measurement methods are highly varied in FSTT studies and associated measurement errors have generally not been paid much attention. Less than half (44%) of 95 FSTT studies comment on measurement error and not all of these provide specific quantification. Where informative error measurement protocols are employed (5% of studies), the mean error magnitudes range from 3% to 45% rTEM and are typically in the order of 10–20%. These values demonstrate that FSTT measurement errors are similar in size to (and likely larger than) the magnitudes of many biological effects being chased. As a result, the attribution of small millimeter or submillimeter differences in FSTT to biological variables must be undertaken with caution, especially where they have not been repeated across different studies/samples. To improve the integrity of FSTT studies and the reporting of FSTT measurement errors, we propose the following standard: (1) calculate the technical error of measurement (TEM or rTEM) in any FSTT research work; (2) assess the error embedded in the full data collection procedure; and (3) conduct validation testing of FSTT means proposed for point estimation prior to publication to ensure newly calculated FSTT means provide improvements. In order to facilitate the latter, a freely available R tool TDValidator that uses the C-Table data for validation testing is provided.

This year (2023) marks 140 years since the first publication of a facial soft tissue thickness (FSTT) study. Since 1883, a total of 139 studies have been published, collectively tallying > 220,000 tissue thickness measurements of > 19,500 adults. In just the last 5-years, 33 FSTT studies have been conducted. Herein, we add these data (plus an additional 20 studies) to the 2018 T-Table to provide an update of > 81,000 new datapoints to the global tallied facial soft tissue depths table. In contrast to the original 2008 T-Table, some notable changes are as follows: increased FSTTs by 3 mm at infra second molar (ecm2–iM2ʹ), 2.5 mm at gonion (go–goʹ), 2 mm at mid-ramus (mr–mrʹ), and 1.5 mm at zygion (zy–zyʹ). Rolling grand means indicate that stable values have been attained for all nine median FSTT landmarks, while six out of nine bilateral landmarks continue to show ongoing fluctuations, indicating further data collection at these landmarks holds value. When used as point estimators for individuals with known values across 24 landmarks (i.e., C-Table data), the updated grand means produce slightly less estimation error than the 2018 T-Table means (3.5 mm versus 3.6 mm, respectively). Future efforts to produce less noisy datasets (i.e., reduce measurement and sampling errors as much as possible between studies) would be useful.

This paper represents the first survey on the application of AI techniques for the analysis of biomedical images with forensic human identification purposes. Human identification is of great relevance in today’s society and, in particular, in medico-legal contexts. As consequence, all technological advances that are introduced in this field can contribute to the increasing necessity for accurate and robust tools that allow for establishing and verifying human identity. We first describe the importance and applicability of forensic anthropology in many identification scenarios. Later, we present the main trends related to the application of computer vision, machine learning and soft computing techniques to the estimation of the biological profile, the identification through comparative radiography and craniofacial superimposition, traumatism and pathology analysis, as well as facial reconstruction. The potentialities and limitations of the employed approaches are described, and we conclude with a discussion about methodological issues and future research.

Facial approximation is a technique that involves constructing the facial muscles and applying a suitable facial soft tissue depth (FSTD) dataset. To date, several FSTD studies have been conducted for varying population groups. This study aims to establish a FSTD dataset of an adult Greek population sample for the first time. The facial depths of subjects were measured on 100 head CT scans of 50 male and 50 female subjects aged from 18 to 99. The 3D head and skull models of subjects were segmented in Amira 6.1 by using histogram method. FSTDs were measured at 22 cranial landmarks (5 mid-sagittal, 17 bilateral). The FSTD dataset was generated by considering the age and sex of subjects. The impact of age and sex on the FSTD was limited. Slight inter-population depth variations were reported. Facial asymmetry calculated between the bilateral landmarks was insignificant for both male and female subjects.

•Overall, >227,400 facial soft tissues have been collected in the literature.•Since 2008, data for >5450 new individuals have been reported.•Herein, updated grand and rolling means are described for the 1883–2017 data.•The grand means triangulate on population parameters for improved accuracy.•The new grand means yield a standard error of the estimate=3.7mm. The tallied facial soft tissue thicknesses (or T-Tables) represent grand means of published facial soft tissue thickness sample means. These sample means have been drawn from across the full-breadth of the facial soft tissue thickness (FSTT) literature, including forensic science, anthropology and odontology. The report of new summary statistics for >1290 new sub-adults and >2200 new adults since the last T-Table calculation, in 2008 for sub-adults and 2013 for adults respectively, makes their update timely. The maximum sample sizes at any landmark now stand at 3023 for individuals aged 0–11 years old (g–g′); 3145 for individuals aged 12–17 years old (n–se′); and 10,333 for adults (n–se′). Following the recalculation of grand weighted means and comparison to the original 2008 data, some shifts in the T-Table statistics are evident at specific landmarks, namely: 2–2.5mm increases at gonion (go–go′) and mid-mandibular border (mmb–mmb′) for adults; 3.5mm decrease at gonion (go–go′) for 12–17year olds; and 2.0mm decrease at menton (me–me′) for 0–11year olds. Differences at all other landmarks (91–100% depending on the dataset) were minimal being <1.0mm. Performance tests of the new grand means as point estimators (using individuals with known FSTT size from the C-Table), show the 2018 T-Table statistics to produce marginally less error than the 2013 means: 2018 standard error of the estimate=3.7mm in contrast to 2013 standard error of the estimate=3.9mm. The long run nature of the T-Table statistics (i.e., big data) and quantified performance test accuracies on known subjects, earmark the 2018 T-Table as the premier FSTT standard for craniofacial identification casework. In the distant future, this is likely to change as the C-Table raw data repository grows, allowing shorths and shormaxes to be calculated for large samples. Given current raw data repository sample sizes of 0–1574 for T-Table landmarks (notably lower for younger individuals), there is some way to go before enhanced central tendency estimators can entirely replace untrimmed arithmetic means.

Facial imaging is a term used to describe methods that use facial images to assist or facilitate human identification. This pertains to two craniofacial identification procedures that use skulls and faces-facial approximation and photographic superimposition-as well as face-only methods for age progression/regression, the construction of facial graphics from eyewitness memory (including composites and artistic sketches), facial depiction, face mapping and newly emerging methods of molecular photofitting. Given the breadth of these facial imaging techniques, it is not surprising that a broad array of subject-matter experts participate in and/or contribute to the formulation and implementation of these methods (including forensic odontologists, forensic artists, police officers, electrical engineers, anatomists, geneticists, medical image specialists, psychologists, computer graphic programmers and software developers). As they are concerned with the physical characteristics of humans, each of these facial imaging areas also falls in the domain of physical anthropology, although not all of them have been traditionally regarded as such. This too offers useful opportunities to adapt established methods in one domain to others more traditionally held to be disciplines within physical anthropology (e.g. facial approximation, craniofacial superimposition and face photo-comparison). It is important to note that most facial imaging methods are not currently used for identification but serve to assist authorities in narrowing or directing investigations such that other, more potent, methods of identification can be used (e.g. DNA). Few, if any, facial imaging approaches can be considered honed end-stage scientific methods, with major opportunities for physical anthropologists to make meaningful contributions. Some facial imaging methods have considerably stronger scientific underpinnings than others (e.g. facial approximation versus face mapping), some currently lie entirely within the artistic sphere (facial depiction), and yet others are so aspirational that realistic capacity to obtain their aims has strongly been questioned despite highly advanced technical approaches (molecular photofitting). All this makes for a broad-ranging, dynamic and energetic field that is in a constant state of flux. This manuscript provides a theoretical snapshot of the purposes of these methods, the state of science as it pertains to them, and their latest research developments.

•The current manuscript can be considered the first standard in the field.•This first study facilitated an international agreement on different aspects of CFS.•The proposed best practices will help practitioners to make a decision on the applicability of CFS in daily forensic caseworks. Craniofacial superimposition, although existing for one century, is still a controversial technique within the scientific community. Objective and unbiased validation studies over a significant number of cases are required to establish a more solid picture on the reliability. However, there is lack of protocols and standards in the application of the technique leading to contradictory information concerning reliability. Instead of following a uniform methodology, every expert tends to apply his own approach to the problem, based on the available technology and deep knowledge on human craniofacial anatomy, soft tissues, and their relationships. The aim of this study was to assess the reliability of different craniofacial superimposition methodologies and the corresponding technical approaches to this type of identification. With all the data generated, some of the most representative experts in craniofacial identification joined in a discussion intended to identify and agree on the most important issues that have to be considered to properly employ the craniofacial superimposition technique. As a consequence, the consortium has produced the current manuscript, which can be considered the first standard in the field; including good and bad practices, sources of error and uncertainties, technological requirements and desirable features, and finally a common scale for the craniofacial matching evaluation. Such a document is intended to be part of a more complete framework for craniofacial superimposition, to be developed during the FP7-founded project MEPROCS, which will favour and standardize its proper application.

•A multiple-lab study on CFS has been carried out for the first time.•Each participant employed her/his particular methodology and technological means.•Provide important insights to better understand the most convenient characteristics. As part of the scientific tasks coordinated throughout The ‘New Methodologies and Protocols of Forensic Identification by Craniofacial Superimposition (MEPROCS)’ project, the current study aims to analyse the performance of a diverse set of CFS methodologies and the corresponding technical approaches when dealing with a common dataset of real-world cases. Thus, a multiple-lab study on craniofacial superimposition has been carried out for the first time. In particular, 26 participants from 17 different institutions in 13 countries were asked to deal with 14 identification scenarios, some of them involving the comparison of multiple candidates and unknown skulls. In total, 60 craniofacial superimposition problems divided in two set of females and males. Each participant follow her/his own methodology and employed her/his particular technological means. For each single case they were asked to report the final identification decision (either positive or negative) along with the rationale supporting the decision and at least one image illustrating the overlay/superimposition outcome. This study is expected to provide important insights to better understand the most convenient characteristics of every method included in this study.