Nicholas Bacci

Introduction: Videogrammetry generates three-dimensional (3D) models from video frames, but reconstruction quality is limited by camera instability and motion blur. This study compared 3D dental models captured with a gimbal-stabilised camera versus a freehand camera.,Methods: Thirty participants were each recorded by two operators, one with a movement disorder and one with stable motor control, using freehand and gimbal-assisted capture (N = 120 models). Frames were extracted at 5 frames per second, classified as blurred using a Laplacian variance threshold of 25, and processed in Agisoft Metashape.,Results: Gimbal stabilisation reduced the median proportion of blurred images from approximately 50–57% to 28–33% (p < 0.001), increased aligned sparse cloud tie points (p < 0.001) and lowered reprojection error (p < 0.001). Gains were larger for the operator with a movement disorder, more than halving the between-operator difference in reprojection error and significantly improving image sharpness, tie point matching, and tie point counts for this operator alone. Dense cloud point counts differed by operator rather than equipment, and Root Mean Square Error (RMSE) did not differ across conditions.,Conclusions: Gimbal stabilisation improves frame-level image quality and reduces operator-dependent variation, offering a low-cost means of standardising capture where recording is time-constrained or physically restricted. RMSE findings are limited by scaling from dental retractors rather than dedicated targets.

While arguments have been made both for and against the value of anatomical eponyms, limited research exists regarding their use by anatomists. As the editors of the principal international anatomy journals have purview and control of terminology in anatomical journals, their perceptions regarding the relevance, ethics and inclusivity of eponym use in anatomy and in publishing in anatomical journals were investigated. A validated, confidential and anonymized self-administered questionnaire which included open-ended questions was distributed to the 22 Editor-in-Chiefs/Senior Editors of anatomical journals. Of the 16 (73%) editors who responded to the survey, only 56% were aware that eponyms had been censured since the time of the 1895 Nomina Anatomica. The majority of responding editors found the use of Latin- and Greek-derived terms more valuable when communicating with students and peers, but also thought eponyms were acceptable in manuscripts submitted to their journals. Thirteen (81%) of the responding editors agreed that eponyms play a vital role in the history of anatomy, and some thought they were important for discussing bioethics concepts. However, 62.5% felt that there were valid reasons for their discontinuation. Half of the respondents did not consider the continued use of eponyms an ethical concern. Responding editors of anatomical journals prefer the use of Latin- and Greek- derived terms when interacting with other anatomists and students. However, the continued use of eponyms was seen as an important opportunity for discussion on the history and ethics of anatomy.

Morphological analysis in forensic facial comparison was recently validated for judicial use. However, no image quality assurance systems exist for this purpose, despite image triage being considered the best practice approach. Hence, this study aimed at testing a semi-quantitative scoring method to assess image quality and investigated facial image resolution and lighting quality quantitatively in a context of forensic facial comparison. For this purpose, 400 facial comparison photographic and CCTV image pools developed from the Wits Face Database were used. These facial images were analysed in prior studies that investigated the validity of morphological analysis. A semi-quantitative image quality scoring system was adapted and tested on the above sample and compared across correct and incorrect matches obtained as part of previous studies using a logistic regression model. In addition, facial images were cropped to the closest pixel comprising the face, head and neck areas; then, a face-to-image pixel proportion was calculated as an estimator of resolution quality; and pixel exposure qualities were obtained to be compared to facial comparison outcomes. Ideal and high image quality scores were related to correctness of matches, while low-quality scores were related to incorrect matches. High pixel proportions were related to true matches and low exposure was related to false positives, while high exposure was related to false negatives. These results suggest that an easy method for image triage could be employed by scoring image quality. Quantitative measures should be investigated further for thresholding quality suitability for confidence of facial comparisons.

Global escalation of crime has necessitated the use of digital imagery to aid the identification of perpetrators. Forensic facial comparison (FFC) is increasingly employed, often relying on poor-quality images. In the absence of standardized criteria, especially in terms of video recordings, verification of the methodology is needed. This paper addresses aspects of FFC, discussing relevant terminology, investigating the validity and reliability of the FISWG morphological feature list using a new South African database, and advising on standards for CCTV equipment. Suboptimal conditions, including poor resolution, unfavorable angle of incidence, color, and lighting, affected the accuracy of FFC. Morphological analysis of photographs, standard CCTV, and eye-level CCTV showed improved performance in a strict iteration analysis, but not when using analogue CCTV images. Therefore, both strict and lenient iterations should be conducted, but FFC must be abandoned when a strict iteration performs worse than a lenient one. This threshold ought to be applied to the specific CCTV equipment to determine its utility. Chance-corrected accuracy was the most representative measure of accuracy, as opposed to the commonly used hit rate. While the use of automated systems is increasing, trained human observer-based morphological analysis, using the FISWG feature list and an Analysis, Comparison, Evaluation, and Verification (ACE-V) approach, should be the primary method of facial comparison.

Disguises are commonly used to mask a person's facial appearance in areas under closed‐circuit television (CCTV) surveillance. While many studies attempted to understand the effects of disguises, such as hats and glasses, on facial recognition, limited studies have looked at disguises in forensic facial comparison. The aim of this study was to compare the outcomes of forensic facial comparison by morphological analysis (MA) in a CCTV sample with sunglasses and brimmed caps. The sample was obtained from the Wits Face Database and organized into 81 face pools of one target facial image wearing a disguise (cap or sunglasses) and 10 potential matching images. MA was conducted across face pools, and confusion matrices were used to assess the outcomes. Surprisingly, sunglasses had limited effect on MA performance both in accuracy (90.4%) and in reliability (κ = 0.798), while caps markedly decreased both accuracy (68.1%) and reliability (κ = 0.639). Error rates were associated primarily with false negatives in both samples (caps: 42.4%; sunglasses: 16.1%) despite the sample distribution favoring false‐positive errors, which were very low (caps: 0.6%; sunglasses: 0%). Similarly to other studies, hats and caps were more harmful to correct identification when compared to sunglasses, which actually resulted in better accuracy than regular CCTV recordings. The effect of brimmed caps on accuracy was attributed to the overall loss of facial information caused. On training analysts, it may be helpful to instruct purposefully avoiding overreliance on easily disguised facial features, as other regions of the face also contain substantial feature information.

Forensic facial comparison is a commonly used, yet under-evaluated method employed in medicolegal contexts across the world. Testing the accuracy and reliability of facial comparisons requires large scale controlled and matching facial image databases. Databases that contain images of individuals on closed-circuit television (CCTV), with matching formal and informal photographs are needed for this type of research. Although many databases are available, the majority if not all are developed in order to improve facial recognition and face detection algorithms through machine learning, with very limited if any measure of standardisation. This paper aims to review the available databases and describe the development of a high resolution, standardised facial photograph and CCTV recording database of male Africans. The database is composed of a total of 6220 standardised and uncontrolled suboptimal facial photographs of 622 matching individuals in five different views, as well as corresponding CCTV footage of 334 individuals recorded under different realistic conditions. A detailed description of the composition and acquisition process of the database as well as its subdivisions and possible uses are provided. The challenges and limitations of developing this database are also highlighted, particularly with regard to obtaining CCTV video recordings and ethics for a database of faces. The application process to access the database is also briefly described.

Between the ever-increasing availability of surveillance evidence and expert-based forensic facial comparison being considered admissible in court, confirming its validity is paramount. Facial comparison is most commonly conducted using morphological analysis (MA), a largely untested feature-based approach. This study aimed at validating the current recommended practice of MA in both standardised and suboptimal surveillance samples. Face pools of 175 South African males were compiled with a series of facial photographs, using images from the Wits Face Database. The first 75 face pools consisted of wildtype (unstandardised) high-quality target photographs, while the remaining 100 face pools consisted of suboptimal closed-circuit television (CCTV) target images. Target images were compared to high-quality standardised photographs. Face pools were analysed using the Facial Identification Scientific Working Group’s guidelines and feature list. Confusion matrices were used to determine the performance of MA in each cohort. MA was found highly accurate (chance-corrected accuracy (CCA): 99.1%) and reliable (κ = 0.921) in the photographic sample and less accurate (CCA: 82.6%) and reliable (κ = 0.743), in the CCTV sample. Higher false-positive and false-negative rates were noted for the CCTV sample, with the majority of errors resulting in false-negative outcomes. The decreased performance in the CCTV sample was attributed to various factors including image quality, angle of recording and lighting. Other studies testing facial comparison identified lower accuracies and reliability across various conditions. Better performance was found here and in other studies that included some form of facial feature list, reinforcing the importance of using a systematic facial feature list.