Craniofacial identification standards: A review of reliability, reproducibility, and implementation.

There are numerous anatomical and anthropometrical standards that can be utilised for craniofacial analysis and identification. These standards originate from a wide variety of sources, such as orthodontic, maxillofacial, surgical, anatomical, anthropological and forensic literature, and numerous media have been employed to collect data from living and deceased subjects. With the development of clinical imaging and the enhanced technology associated with this field, multiple methods of data collection have become accessible, including Computed Tomography, Cone-Beam Computed Tomography, Magnetic Resonance Imaging, Radiographs, Three-dimensional Scanning, Photogrammetry and Ultrasound, alongside the more traditional in vivo methods, such as palpation and direct measurement, and cadaveric human dissection. Practitioners often struggle to identify the most appropriate standards and research results are frequently inconsistent adding to the confusion. This paper aims to clarify how practitioners can choose optimal standards, which standards are the most reliable and when to apply these standards for craniofacial identification. This paper describes the advantages and disadvantages of each mode of data collection and collates published research to review standards across different populations for each facial feature. This paper does not aim to be a practical instruction paper; since this field encompasses a wide range of 2D and 3D approaches (e.g., clay sculpture, sketch, automated, computer-modelling), the implementation of these standards is left to the individual practitioner.

Relationship between bite force, occlusal contact area, and three-dimensional facial soft tissue in dentofacial deformities.

PURPOSE: This study aimed to investigate three-dimensional facial soft tissue dimensions, maximum bite force (MBF), and occlusal contact area in patients with DFD. In addition, we analyzed the relationship between MBF and the three-dimensional facial measurements. METHODS: Thirty-two patients with skeletal Class III DFD and 20 patients with Class II DFD underwent a soft tissue evaluation using surface laser scanning, as well as MBF and occlusal contact area assessments. The DFD groups were compared with each other and with 25 healthy subjects. RESULTS: Significant morphological differences were found in the transversal, vertical, and anteroposterior dimensions between Class II DFD and Class III DFD. Both DFD groups presented an increased linear distance of chin height, which was strongly related with decreased MBF magnitude. The DFD groups exhibited lower MBF and occlusal contact area, with no significant differences between Class II and Class III DFD. CONCLUSION: The presence of DFD affected 3D measurements of facial soft tissue, causing variations beyond normal limits, lower MBF, and occlusal contact area in both Class II and Class III DFD patients. The vertical dimension might have influenced the lower MBF magnitude in the studied skeletal deformities.

The relevance and accuracy of an AI algorithm-based descriptor on 23 facial attributes in a diverse female US population.

BACKGROUND: The response of AI in situations that mimic real life scenarios is poorly explored in populations of high diversity. OBJECTIVE: To assess the accuracy and validate the relevance of an automated, algorithm-based analysis geared toward facial attributes devoted to the adornment routines of women. METHODS: In a cross-sectional study, two diversified groups presenting similar distributions such as age, ancestry, skin phototype, and geographical location was created from the selfie images of 1041 female in a US population. 521 images were analyzed as part of a new training dataset aimed to improve the original algorithm and 520 were aimed to validate the performance of the AI. From a total 23 facial attributes (16 continuous and 7 categorical), all images were analyzed by 24 make-up experts and by the automated descriptor tool. RESULTS: For all facial attributes, the new and the original automated tool both surpassed the grading of the experts on a diverse population of women. For the 16 continuous attributes, the gradings obtained by the new system strongly correlated with the assessment made by make-up experts (r ≥ 0.80; p < 0.0001) and supported by a low error rate. For the seven categorical attributes, the overall accuracy of the AI-facial descriptor was improved via enrichment of the training dataset. However, some weaker performance in spotting specific facial attributes were noted. CONCLUSION: In conclusion, the AI-automatic facial descriptor tool was deemed accurate for analysis of facial attributes for diverse women although some skin complexion, eye color, and hair features required some further finetuning.

Automated reconstruction: Predictive models based on facial morphology matrices.

Forensic Facial Approximation (FFA) has evolved, with techniques advancing to refine the intercorrelation between the soft-tissue facial profile and the underlying skull. FFA has become essential for identifying unknown persons in South Africa, where the high number of migrant and illegal labourers and many unidentified remains make the identification process challenging. However, existing FFA methods are based on American or European standards, rendering them inapplicable in a South African context. We addressed this issue by conducting a study to create prediction models based on the relationships between facial morphology and known factors, such as population affinity, sex, and age, in white South African and French samples. We retrospectively collected 184 adult cone beam computed tomography (CBCT) scans representing 76 white South Africans (29 males and 47 females) and 108 French nationals (54 males and 54 females) to develop predictive statistical models using a projection onto latent structures regression algorithm (PLSR). On training and untrained datasets, the accuracy of the estimated soft-tissue shape of the ears, eyes, nose, and mouth was measured using metric deviations. The predictive models were optimized by integrating additional variables such as sex and age. Based on trained data, the prediction errors for the ears, eyes, nose, and mouth ranged between 1.6 mm and 4.1 mm for white South Africans; for the French group, they ranged between 1.9 mm and 4.2 mm. Prediction errors on non-trained data ranged between 1.6 mm and 4.3 mm for white South Africans, whereas prediction errors ranging between 1.8 mm and 4.3 mm were observed for the French. Ultimately, our study provided promising predictive models. Although the statistical models can be improved, the inherent variability among individuals restricts the accuracy of FFA. The predictive validity of the models was improved by including sex and age variables and considering population affinity. By integrating these factors, more customized and accurate predictive models can be developed, ultimately strengthening the effectiveness of forensic analysis in the South African region.

A novel approach to the detection of facial wrinkles: Database, detection algorithm, and evaluation metrics.

Skin wrinkles result from intrinsic aging processes and extrinsic influences, including prolonged exposure to ultraviolet radiation and tobacco smoking. Hence, the identification of wrinkles holds significant importance in skin aging and medical aesthetic investigation. Nevertheless, current methods lack the comprehensiveness to identify facial wrinkles, particularly those that may appear insignificant. Furthermore, the current assessment techniques neglect to consider the blurred boundary of wrinkles and cannot differentiate images with varying resolutions. This research introduces a novel wrinkle detection algorithm and a distance-based loss function to identify full-face wrinkles. Furthermore, we develop a wrinkle detection evaluation metric that assesses outcomes based on curve, location, and gradient similarity. We collected and annotated a dataset for wrinkle detection consisting of 1021 images of Chinese faces. The dataset will be made publicly available to further promote wrinkle detection research. The research demonstrates a substantial enhancement in detecting subtle wrinkles through implementing the proposed method. Furthermore, the suggested evaluation procedure effectively considers the indistinct boundaries of wrinkles and is applicable to images with various resolutions.

Reliability and validity of handheld structured light scanners and a static stereophotogrammetry system in facial three-dimensional surface imaging.

Several new systems for three-dimensional (3D) surface imaging of the face have become available to assess changes following orthognathic or facial surgery. Before they can be implemented in practice, their reliability and validity must be established. Our aim, therefore, was to study the intra- and inter-system reliability and validity of 3dMD (stereophotogrammetry), Artec Eva and Artec Space Spider (both structured light scanners). Intra- and inter-system reliability, expressed in root mean square distance, was determined by scanning a mannequin's head and the faces of healthy volunteers multiple times. Validity was determined by comparing the linear measurements of the scans with the known distances of a 3D printed model. Post-processing errors were also calculated. Intra-system reliability after scanning the mannequin's head was best with the Artec Space Spider (0.04 mm Spider; 0.07 mm 3dMD; 0.08 mm Eva). The least difference in inter-system reliability after scanning the mannequin's head was between the Artec Space Spider and Artec Eva. The best intra-system reliability after scanning human subjects was with the Artec Space Spider (0.15 mm Spider; 0.20 mm Eva; 0.23 mm 3dMD). The least difference in inter-system reliability after scanning human subjects was between the Artec Eva and Artec Space Spider. The most accurate linear measurement validity occurred with the Artec Space Spider. The post-processing error was 0.01 mm for all the systems. The Artec Space Spider is the most reliable and valid scanning system.

Body mass index is an overlooked confounding factor in existing clustering studies of 3D facial scans of children with autism spectrum disorder.

Cluster analyzes of facial models of autistic patients aim to clarify whether it is possible to diagnose autism on the basis of facial features and further to stratify the autism spectrum disorder. We performed a cluster analysis of sets of 3D scans of ASD patients (116) and controls (157) using Euclidean and geodesic distances in order to recapitulate the published results on the Czech population. In the presented work, we show that the major factor determining the clustering structure and consequently also the correlation of resulting clusters with autism severity degree is body mass index corrected for age (BMIFA). After removing the BMIFA effect from the data in two independent ways, both the cluster structure and autism severity correlations disappeared. Despite the fact that the influence of body mass index (BMI) on facial dimensions was studied many times, this is the first time to our knowledge when BMI was incorporated into the faces clustering study and it thereby casts doubt on previous results. We also performed correlation analysis which showed that the only correction used in the existing clustering studies-dividing the facial distance by the average value within the face-is not eliminating correlation between facial distances and BMIFA within the facial cohort.

High-frequency Ultrasound in the Assessment before and after Applying HArmonyCa™.

AIM: To describe a clinical case of ultrasound (US) used to evaluate, before, post-immediately, and after 4 months, the facial application of a volumizing and biostimulating substance. BACKGROUND: Detecting the behavior of injected filler materials with high-frequency US-guided application is the future of natural facial rejuvenation with more predictable and satisfactory results. TECHNIQUE: A patient indicated for orofacial harmonization (OFH) procedures through volumizing and biostimulating material application was invited to participate. The technique was performed by applying HArmonyCa™ (Allergan Aesthetics, Irvine, CA, USA) in the gonial, preauricular, and bilateral lateral zygomatic angle regions. The first evaluations used the US images before and after product application with a Logiq e® high-frequency US device (GE Healthcare, Chicago, IL, USA) with a probe/linear transducer of 18 MHz. About 4 months after the procedure, a new assessment with the same initial acquisition pattern was performed. The first evaluation showed normal-looking anatomical structures without the esthetic material. Immediately after the procedure and 4 months later, the assessments presented semi-permanent esthetic fillers as dispersed lobulated hyperechogenic areas with a cloud aspect. CONCLUSION: High-frequency US was efficient in the static evaluation of HArmonyCa™ behavior on the facial skin. CLINICAL SIGNIFICANCE: The US-guided application of injectable products in specific areas has minimal side effects and contributes to more predictable and satisfactory results. How to cite this article: Gouveia RSA, Tostes LLL, Bezerra FV, et al. High-frequency Ultrasound in the Assessment before and after Applying HArmonyCa™. J Contemp Dent Pract 2024;25(1):10-14.

Fully automated landmarking and facial segmentation on 3D photographs.

Three-dimensional facial stereophotogrammetry provides a detailed representation of craniofacial soft tissue without the use of ionizing radiation. While manual annotation of landmarks serves as the current gold standard for cephalometric analysis, it is a time-consuming process and is prone to human error. The aim in this study was to develop and evaluate an automated cephalometric annotation method using a deep learning-based approach. Ten landmarks were manually annotated on 2897 3D facial photographs. The automated landmarking workflow involved two successive DiffusionNet models. The dataset was randomly divided into a training and test dataset. The precision of the workflow was evaluated by calculating the Euclidean distances between the automated and manual landmarks and compared to the intra-observer and inter-observer variability of manual annotation and a semi-automated landmarking method. The workflow was successful in 98.6% of all test cases. The deep learning-based landmarking method achieved precise and consistent landmark annotation. The mean precision of 1.69 ± 1.15 mm was comparable to the inter-observer variability (1.31 ± 0.91 mm) of manual annotation. Automated landmark annotation on 3D photographs was achieved with the DiffusionNet-based approach. The proposed method allows quantitative analysis of large datasets and may be used in diagnosis, follow-up, and virtual surgical planning.

Establishment and verification of a method for analyzing nasal blackheads images.

BACKGROUND: As people pay more attention to their skin health and the demand of developing skin care products for facial blackheads grows, the value of objective and efficient image recognition methods for blackheads is becoming more evident. Inspired by this current situation, this study attempted to analyze the number of blackheads of different severity automatically on the nose using an object recognition method on photographs of the nasal blackheads of subjects. METHOD: This study collected 350 subjects' facial photos in the laboratory environment, who aged 18-60, with blackhead symptoms in the nasal region. And expert assessment was used as a reference for machine learning to verify the performance of the nasal blackhead image recognition model through consistency and correlation analysis. RESULTS: The study concluded that the algorithm accuracy reached above 0.9, the model itself was effective, and the consistency between the model and the expert assessor assessment results was good, with the number of nasal blackheads, the count of blackheads of different severity, and the intra-group correlation coefficient ICC of blackhead severity all above 0.9, indicating that the deep learning-based assessment model had high overall performance and the evaluation results were comparable to those of the expert assessor. CONCLUSION: The recognition and analyzing model of nasal blackhead images provides a scientifically objective and accurate method for identifying the number and evaluating the severity of nasal blackheads. By using this model, the efficiency of evaluating nasal blackhead images in the cosmetics clinical trial will be improved. The assessment result of nasal blackheads will be objective and stable, and not only rely on the professional knowledge and clinical experience of assessors. The model can try to be applied in cosmetics efficacy testing and continuously optimized.

Facial soft-tissue thickness in children: A study of a CTBC Brazilian sample.

As an auxiliary method in the process of human identification, forensic facial approximation (FFA) is an important tool for identifying unknown human bodies whose remains do not present the necessary traceability to any antemortem data collection. Specific characteristics are necessary when addressing children aged between 6 and 10 years, who have little sexual differentiation and a mixed dentition. Due to the chronology of eruption of the permanent second molars in this population, it is not possible to measure facial soft-tissue thickness (FSTT) from specific landmarks such as supra and infra M2. The objective of this research was to report the method for measuring the average FSTT of 32 landmarks adapting the method for adults replacing the landmarks at the upper and lower second molars (Supra M2 and Infra M2) in children up to 10 years of age for a measurement using the deciduous second molars as reference. We found statistical differences for some points, considering the variables of age and sex, but with a maximum difference of 2 mm, which allows the use of a single FSTT table. The deciduous teeth can replace the reference of the thicknesses at the supra and infra M2 landmarks. In addition to the new FSTT data for children in Brazil, we concluded that the proposed adaptation to the deciduous M2 points can be applied to obtain soft-tissue data for 32 facial points.

Three-dimensional facial soft-tissue changes after surgical orthodontics in different vertical facial types of skeletal Class III malocclusion: A retrospective study.

The study compared the soft-tissue response to hard-tissue movement among different Class III vertical facial types after orthognathic surgery (OGS). The study included 90 consecutive adult patients with skeletal Class III malocclusion who underwent two-jaw OGS. Patients were divided into three groups (high, medium, and low angle) based on the presurgical Frankfort-mandibular plane angle. Cone-beam computerized tomographs were taken before surgery and after debonding. Soft- and hard-tissue linear and angular measurements were performed using three-dimensional reconstruction images. One-way analysis of variance was used for intergroup comparisons. Soft tissue tended to respond more to hard-tissue movement in the lower lip area in patients with low angle (mean = 0.089, SD = 0.047, p = 0.023), whereas no significant difference was observed for other sites. Consistently, L1/Li thickness increased most significantly in the high-angle group (mean = 1.98, SD = 2.14, p = 0.0001), and B/Si thickness decreased most significantly after surgery (mean = 2.16, SD = 2.68, p = 0.016). The findings suggest that the high-angle group had a higher chance of undergoing genioplasty to enhance chin contour. Different OGS plans should be considered for different Class III vertical facial types.

Skin patch based makeup finish assessment technique by deep neural network.

BACKGROUND: Skin color and texture play a significant role in influencing impressions. To understand the influence of skin appearance and to develop better makeup products, objective evaluation methods for makeup finish have been explored. This study aims to apply machine learning technology, specifically deep neural network (DNN), to accurately analyze and evaluate delicate and complex cosmetic skin textures. METHODS: "Skin patch datasets" were extracted from facial images and used to train a DNN model. The advantages of using skin patches include retaining fine texture, eliminating false correlations from non-skin features, and enabling visualization of the inferred results for the entire face. The DNN was trained in two ways: a classification task to classify skin attributes and a regression task to predict the visual assessment of experts. The trained DNNs were applied for the evaluation of actual makeup conditions. RESULTS: In the classification task training, skin patch-based classifiers for age range, presence or absence of base makeup, formulation type (powder/liquid) of the applied base makeup, and immediate/while after makeup application were developed. The trained DNNs on regression task showed high prediction accuracy for the experts' visual assessment. Application of DNN to the evaluation of actual makeup conditions clearly showed appropriate evaluation results in line with the appearance of the makeup finish. CONCLUSION: The proposed method of using DNNs trained on skin patches effectively evaluates makeup finish. This approach has potential applications in visual science research and cosmetics development. Further studies can explore the analysis of different skin conditions and the development of personalized cosmetics.

Enhancing Face Recognition With Detachable Self-Supervised Bypass Networks.

Attributed to the development of deep networks and abundant data, automatic face recognition (FR) has quickly reached human-level capacity in the past few years. However, the FR problem is not perfectly solved in case of large poses and uncontrolled occlusions. In this paper, we propose a novel bypass enhanced representation learning (BERL) method to improve face recognition under unconstrained scenarios. The proposed method integrates self-supervised learning and supervised learning together by attaching two auxiliary bypasses, a 3D reconstruction bypass and a blind inpainting bypass, to assist robust feature learning for face recognition. Among them, the 3D reconstruction bypass enforces the face recognition network to encode pose independent 3D facial information, which enhances the robustness to various poses. The blind inpainting bypass enforces the face recognition network to capture more facial context information for face inpainting, which enhances the robustness to occlusions. The whole framework is trained in end-to-end manner with two self-supervised tasks above and the classic supervised face identification task. During inference, the two auxiliary bypasses can be detached from the face recognition network, avoiding any additional computational overhead. Extensive experimental results on various face recognition benchmarks show that, without any cost of extra annotations and computations, our method outperforms state-of-the-art methods. Moreover, the learnt representations can also well generalize to other face-related downstream tasks such as the facial attribute recognition with limited labeled data.

Correspondence attention for facial appearance simulation.

In orthognathic surgical planning for patients with jaw deformities, it is crucial to accurately simulate the changes in facial appearance that follow the bony movement. Compared with the traditional biomechanics-based methods like the finite-element method (FEM), which are both labor-intensive and computationally inefficient, deep learning-based methods offer an efficient and robust modeling alternative. However, current methods do not account for the physical relationship between facial soft tissue and bony structure, causing them to fall short in accuracy compared to FEM. In this work, we propose an Attentive Correspondence assisted Movement Transformation network (ACMT-Net) to predict facial changes by correlating facial soft tissue changes with bony movement through a point-to-point attentive correspondence matrix. To ensure efficient training, we also introduce a contrastive loss for self-supervised pre-training of the ACMT-Net with a k-Nearest Neighbors (k-NN) based clustering. Experimental results on patients with jaw deformities show that our proposed solution can achieve significantly improved computational efficiency over the state-of-the-art FEM-based method with comparable facial change prediction accuracy.

Disastrous cerebral and ocular vascular complications after cosmetic facial filler injections: a retrospective case series study.

Soft tissue filler injections are among the most popular facial rejuvenation methods. Cerebral infarction and ophthalmic artery occlusion are rare and catastrophic complications, especially when facial cosmetic fillers are injected by inexperienced doctors. Radiologists and plastic surgeons need to increase their awareness of the complications associated with fillers, which allows early diagnosis and intervention to improve patient prognosis. Regarding the mechanism by which vascular occlusion occurs after facial filler injections, a retrograde embolic mechanism is currently the predominant theory. Numerous case reports have been presented regarding complications associated with injections of facial aesthetics. However, the small sample sizes of these studies did not allow for an adequate assessment of the clinical and imaging manifestations based on the location of the occlusion and the type of filler, and detailed elaboration of multiple cerebral infarctions is also lacking. Therefore, this study aimed to investigate the clinical and radiological features of severe cerebral and ocular complications caused by cosmetic facial filler injections. In addition, we discuss the pathogenesis, treatment, and prognosis of these patients. The clinical, computed tomography (CT), magnetic resonance imaging (MRI), and digital subtraction angiography (DSA) findings were described and analysed. Radiological examinations are crucial for demonstrating severe complications, and brain MRI is especially strongly suggested for patients with cosmetic filler-induced vision loss to identify asymptomatic cerebral infarctions. Extreme caution and care should be taken during facial injections by plastic surgeons.

Automatic labeling of facial zones for digital clinical application: An ensemble of semantic segmentation models.

INTRODUCTION: The application of artificial intelligence to facial aesthetics has been limited by the inability to discern facial zones of interest, as defined by complex facial musculature and underlying structures. Although semantic segmentation models (SSMs) could potentially overcome this limitation, existing facial SSMs distinguish only three to nine facial zones of interest. METHODS: We developed a new supervised SSM, trained on 669 high-resolution clinical-grade facial images; a subset of these images was used in an iterative process between facial aesthetics experts and manual annotators that defined and labeled 33 facial zones of interest. RESULTS: Because some zones overlap, some pixels are included in multiple zones, violating the one-to-one relationship between a given pixel and a specific class (zone) required for SSMs. The full facial zone model was therefore used to create three sub-models, each with completely non-overlapping zones, generating three outputs for each input image that can be treated as standalone models. For each facial zone, the output demonstrating the best Intersection Over Union (IOU) value was selected as the winning prediction. CONCLUSIONS: The new SSM demonstrates mean IOU values superior to manual annotation and landmark analyses, and it is more robust than landmark methods in handling variances in facial shape and structure.

Evaluation of the anterior dentoalveolar relationship in skeletal Class III malocclusion patients with different vertical facial patterns using cone-beam computed tomography.

OBJECTIVES: To measure and compare labiolingual inclinations of the teeth and alveolar bone and the anterior dentoalveolar inclination in patients with skeletal Class III malocclusions with different vertical facial patterns using cone-beam computed tomography (CBCT). MATERIALS AND METHODS: Based on the inclusion and exclusion criteria, 84 CBCT images of patients with untreated skeletal Class III malocclusion were selected. There were 28 patients each in the hypo-, normo-, and hyperdivergent groups. The labiolingual inclinations of the teeth, the corresponding alveolar bone, and the anterior dentoalveolar inclinations were measured and analyzed statistically. RESULTS: The inclinations of the mandibular canine and corresponding alveolar bone were smaller in the hypodivergent group than in the hyperdivergent group. The inclination of the alveolar bone and the maxillary dentoalveolar inclination were smaller in the hyperdivergent group than in the hypodivergent group. CONCLUSIONS: There were differences in the inclination of the teeth, corresponding alveolar bone, and dentoalveolar inclinations at different positions among skeletal Class III patients with different vertical facial patterns. The roots were generally located on the labial side of the alveolar bone.

Differences in the annotation between facial images and videos for training an artificial intelligence for skin type determination.

BACKGROUND: The Grand-AID research project, consisting of GRANDEL-The Beautyness Company, the dermatology department of Augsburg University Hospital and the Chair of IT Infrastructure for Translational Medical Research at Augsburg University, is currently researching the development of a digital skin consultation tool that uses artificial intelligence (AI) to analyze the user's skin and ultimately perform a personalized skin analysis and a customized skin care routine. Training the AI requires annotation of various skin features on facial images. The central question is whether videos are better suited than static images for assessing dynamic parameters such as wrinkles and elasticity. For this purpose, a pilot study was carried out in which the annotations on images and videos were compared. MATERIALS AND METHODS: Standardized image sequences as well as a video with facial expressions were taken from 25 healthy volunteers. Four raters with dermatological expertise annotated eight features (wrinkles, redness, shine, pores, pigmentation spots, dark circles, skin sagging, and blemished skin) with a semi-quantitative and a linear scale in a cross-over design to evaluate differences between the image modalities and between the raters. RESULTS: In the videos, most parameters tended to be assessed with higher scores than in the images, and in some cases significantly. Furthermore, there were significant differences between the raters. CONCLUSION: The present study shows significant differences between the two evaluation methods using image or video analysis. In addition, the evaluation of the skin analysis depends on subjective criteria. Therefore, when training the AI, we recommend regular training of the annotating individuals and cross-validation of the annotation.

The Doppelgänger effect? A comparative study of forensic facial depiction methods.

This study attempted to assess the reproducibility of 2D and 3D forensic methods for facial depiction from skeletal remains (2D sketch, 3D manual, 3D automated, 3D computer-assisted). In a blind study, thirteen practitioners produced fourteen facial depictions, using the same skull model derived from CT data of a living donor, a biological profile and relevant soft tissue data. The facial depictions were compared to the donor subject using three different evaluation methods: 3D geometric, 2D face recognition ranking and familiar resemblance ratings. Five of the 3D facial depictions (all 3D methods) demonstrated a deviation error within ± 2 mm for ≥ 50% of the total face surface. Overall, no single 3D method (manual, computer assisted, automated) produced consistently high results across all three evaluations. 2D comparisons with a facial photograph of the donor were carried out for all the 2D and 3D facial depictions using four freely available face recognition algorithms (Toolpie; Photomyne; Face ++; Amazon). The 2D sketch method produced the highest ranked matches to the donor photograph, with overall ranking in the top six. Only one 3D facial depiction was ranked highly in both the 3D geometric and 2D face recognition comparisons. The majority (67%) of the facial depictions were rated as limited or moderate resemblance by the familiar examiner. Only one 2D facial depiction was rated as strong resemblance, whilst two 2D sketches and two 3D facial depictions were rated as good resemblances by the familiar examiner. The four most geometrically accurate 3D facial depictions were only rated as limited or moderate resemblance to the donor by the familiar examiner. The results suggest that where a consistent facial depiction method is utilised, we can expect relatively consistent metric reliability between practitioners. However, presentation standards for practitioners would greatly enhance the possibility of recognition in forensic scenarios.