The juvenile face as a suitable age indicator in child pornography cases: a pilot study on the reliability of automated and visual estimation approaches

Accuracy of geometric morphometrics for age estimation using frontal face photographs of children and adolescents: A promising method for forensic practice

Age estimation is crucial in legal and humanitarian contexts. Forensic professionals may use various procedures to estimate age, including dental analysis, bone density tests, evaluation of physical characteristics including facial bone structure and development, and image-based methods. Although images are often the only material available, visual observation of photographic material is an imprecise method in age estimation, which can compromise judicial decision-making. Analyzing 4000 photographs from the Brazilian Federal Police database, representing four age groups (6, 10, 14, and 18 years), the study employed automated analysis by marking 28 photogrammetric points. Data were used to establish facial patterns by age and sex using the facial geometric morphometrics method. Performance was assessed through a Multinomial Logistic Regression model, evaluating accuracy, sensitivity, and specificity across the categorical age groups. Analyses were conducted using R software, with a 5 % significance level. The study found that facial geometric morphometrics achieved an overall accuracy of 69.3 % in age discrimination, with higher accuracy in males (74.7 %) compared to females (65.8 %) (p < 0.001). The method excelled at predicting the age of 6-year-olds with 87.3 % sensitivity and 95.6 % specificity but had lower performance at 14 years. It showed greater accuracy in distinguishing age groups with larger age gaps, achieving up to 99.5 % accuracy between certain groups, and was particularly effective in differentiating ages of 6 and 10 years in females and 10, 14, and 18 years in males. The facial geometric morphometrics emerges as a promising approach for age estimation among children and adolescents in forensic settings.

The assessment of the biological age of children`s characters created in the convention of Japanese animation in forensic practice

Introduction: According to criminal codes of most Western countries, possessing, producing and disseminating of fictional paedopornography is a crime. In light of these laws, the shotacon/lolicon (popular and widely available Japanese animations or comic books showing minors in a sexual context) seems to deserve special mention. There have been several convictions for violations of these laws, however, the methodology of a depicted person’s age estimation is still unestablished. The aim of this study was to assess the suitability of anthropometrical prediction of age to the analysis of characters animated in the Japanese style. Material and methods: The metric (distance between facial landmarks) and non-metric (type of chin shape) features of 173 animated characters’ faces were obtained. Material was collected from 90 most popular Japanese anime series. Measurements were conducted in ImageJ software. The correlations of age and standardized measurements: en-ex, en-en, eye height, pu-prn, pu-sto, pu-gn were examined. The chin shape was described by three independent ‘judges’. Results and conclusions: Correlations for pu-prn, pu-sto, pu-gn and eye height in females and in all males were statistically significant. Age prediction was made using linear regression equations. Good prediction (± 1 year) was obtained for 44% males and 17% females. Prediction within the acceptable range (± 2 years) was achieved for 23% of males and 18% of females. In total, the prediction with an error of no more than ± 2 years was obtained for 67% of males and 35% from females, which is comparable to the results obtained in the study of real children. Moreover, triangular or rounded chin shape was significantly more frequent in boys aged 10–12 years, and square in older boys 16–18 years. Current research provides a basis for developing a methodology for assessing the age of animated characters. There is a need for further research in this area.

Diagnostic Features for Human Categorisation of Adult and Child Faces

Knowing how humans differentiate children from adults has useful implications in many areas of both forensic and cognitive psychology. Yet, how we extract age from faces has been surprisingly underexplored in both disciplines. Here, we used a novel data-driven experimental technique to objectively measure the facial features human observers use to categorise child and adult faces. Relying on more than 35,000 trials, we used a reverse correlation technique that enabled us to reveal how specific features which are known to be important in face-perception - position, spatial-frequency (SF), and orientation - are associated with accurate child and adult discrimination. This showed that human observers relied on evidence in the nasal bone and eyebrow area for accurate adult categorisation, while they relied on the eye and jawline area to accurately categorise child faces. For orientation structure, only facial information of vertical orientation was linked to face-adult categorisation, while features of horizontal and, to a lesser extent oblique orientations, were more diagnostic of a child face. Finally, we found that SF diagnosticity showed a U-shaped pattern for face-age categorisation, with information in low and high SFs being diagnostic of child faces, and mid SFs being diagnostic of adult faces. Through this first characterisation of the facial features of face-age categorisation, we show that important information found in psychophysical studies of face-perception in general (i.e., the eye area, horizontals, and mid-level SFs) is crucial to the practical context of face-age categorisation, and present data-driven procedures through which face-age classification training could be implemented for real-world challenges.

Adjusted binary classification (ABC) model in forensic science: An example on sex classification from handprint dimensions

Binary classification techniques are commonly used in forensic examination to test if a specimen belongs to a particular group and base the expert opinion on the questioned evidence. However, most of the currently used methods do not achieve sufficient accuracy due to the ignoring of the specimens classified in the overlapping area. To address the issue, we proposed a novel Adjusted binary classification (ABC) algorithm that automatically adjusts posterior probabilities to reach classification accuracy and positive/negative predicted values (PPV, NPV) of 95 %. In the presented example, we used three handprint measurements from 160 participants (80 males and 80 females) to develop models that would classify sex from their dimensions. The sample was split into the training/cross-validated (70 %) and testing sample (30 %). We developed four classification models using linear discriminant analysis (LDA) by employing traditional single cut-off values and ABC approach that for each group provides a specific posterior probability cut-off threshold. In the cross-validated sample, the accuracy of traditional models was 78.7-92.5 %, while PPVs/NPVs ranged between 78.2 and 93 %. ABC models provided 95 % accuracy, PPV, and NPV, and could classify 35.5-88.1 % of specimens. In the testing sample, ABC models achieved accuracy of 97.3-100 %, PPV/NPV 95.4-100 %, and could be applied to 29.1-87.5 % of specimens. The study demonstrated that the ABC approach could adjust classification models to reach predefined values of accuracy, PPV, and NPV. Therefore, it could be an efficient tool for conducting a binary classification in forensic settings and minimizing the possibilities of incorrect classifications.

Dental age estimation: Demirjian's versus the other methods in different populations. A literature review

Age estimation is an important issue in forensic anthropology and odontology, since it is applied to both the living and the dead. Studies have shown that assessing tooth development using radiological methods is a reliable way to estimate age, especially in children. Demirjian's method is the most widely used estimation method for dental age evaluation. Over the years, many methods have been developed in order to asses which one predicts more accurately the age. In the literature, many populations were examined with these different methods and discordant results are described. This review aims to evaluate the studies in which Demirjian's method was compared with Willems', Cameriere's, Nolla's, Smith's, Haavikko's and Chaillet's methods in order to estimate the most accurate dental age estimation method in different populations. Using the methodology described in the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement, the authors conducted a review of existing literature using three databases: PubMed, Google Scholar and Google Scopus.

Age verification using random forests on facial 3D landmarks

Three-dimensional facial images are becoming more and more widespread. As such images provide more information about facial morphology than 2D imagery, they show great promise for use in future forensic applications, including age estimation and verification. This paper proposes an approach using random forests, a machine learning method, to develop and test models for classification of legal age thresholds (15 years and 18 years) using 3D facial landmarks. Our approach was developed on a set of 3D facial scans from 394 Czech individuals (194 males and 200 females) aged between 10 and 25 years. The dataset was retrieved from a sizable database of Central European faces - The FIDENTIS 3D Face Database. Three main types of input variables were processed using random forests: I) shape (size-invariant) coordinates of 3D landmarks, II) size and shape coordinates of 3D landmarks, and III) inter-landmark distances, angles and indices. The performance rates for the combinations of variables and age threshold were expressed in terms of sensitivity and specificity. The overall accuracy rates varied from 71.4%-91.5% (when the male and female samples were pooled). In general, higher accuracy was achieved for the age limit of 18 years than for 15 years. Whereas size-variant variables showed a better performance rate for the age limit of 15 years, the size-invariant variables (i.e., shape variables) were better for classifying individuals under 18 years. The verification models grounded on traditional variables (distances, angles, indices) yielded consistently higher performance rates on females than on males, whereas the inverse trend was observed for the models built on 3D coordinates. The results indicate that age verification based on 3D facial data with processing by the random forests method has high potential for further forensic or biometric applications.

Evaluation of secondary dentin deposition in lower first molars to indicate a legal age threshold of 14 years using receiver operating characteristic curves

The age threshold of 14 years has become vital in proving legal violations involving children, particularly in cases of suspected child labour, child pornography and the minimum age of criminal responsibility. In recent years, there has been great interest in the evaluation of age in children and sub-adults using analysis of regressive changes in teeth, with a particular focus on age thresholds that are of medico-legal importance. This research aimed to compare the sensitivity and specificity of stages of root pulp visibility by Olze et al. in a sample of South Indian children aged between 12 and 16 years, with an age threshold of 14 years, using receiver operating characteristic curves and the area under the curve (AUC). Among the four stages of root pulp visibility, stage 2 showed the highest AUC in both female and male cohorts. For stage 2, lower sensitivity and higher specificity values were recorded, indicating the possibility of type II errors (i.e. false negatives). In both sexes, stage 2 had a higher AUC (i.e. 0.696 in females and 0.706 in males, respectively). Based on our findings, it can be concluded that this staging method in lower first molars is of limited value in indicating the legal age threshold of 14 years. Future research should validate the proposed approach in a larger sample and consider how to improve predictions in this area.

Estimating sex and age from a face: a forensic approach using machine learning based on photo-anthropometric indexes of the Brazilian population

The facial analysis permits many investigations, some of the most important of which are craniofacial identification, facial recognition, and age and sex estimation. In forensics, photo-anthropometry describes the study of facial growth and allows the identification of patterns in facial skull development, for example, by using a group of cephalometric landmarks to estimate anthropological information. Previous works presented, as indirect applications, the use of photo-anthropometric measurements to estimate anthropological information such as age and sex. In several areas, automation of manual procedures has achieved advantages over and similar measurement confidence as a forensic expert. This manuscript presents an approach using photo-anthropometric indexes, generated from frontal faces cephalometric landmarks of the Brazilian population, to create an artificial neural network classifier that allows the estimation of anthropological information, in this specific case age and sex. This work is focused on four tasks: (i) sex estimation on ages from 5 to 22 years old, evaluating the interference of age on sex estimation; (ii) age estimation from photo-anthropometric indexes for four age intervals (1 year, 2 years, 4 years, and 5 years); (iii) age group estimation for thresholds of over 14 and over 18 years old; and; (iv) the provision of a new data set, available for academic purposes only, with a large and complete set of facial photo-anthropometric points marked and checked by forensic experts, measured from over 18,000 faces of individuals from Brazil over the last 4 years. The proposed binary classifier obtained significant results, using this new data set, for the sex estimation of individuals over 14 years old, achieving accuracy values higher than 0.85 by the F₁ measure. For age estimation, the accuracy results are 0.72 for the F₁ measure with an age interval of 5 years. For the age group estimation, the F₁ measures of accuracy are higher than 0.93 and 0.83 for thresholds of 14 and 18 years, respectively.

The Challenges of Identifying and Classifying Child Sexual Abuse Material

The aim of the present study was to (a) assess the reliability with which indecent images of children (IIOC) are classified as being of an indecent versus nonindecent nature, and (b) examine in detail the decision-making process engaged in by law enforcement personnel who undertake the difficult task of identifying and classifying IIOC as per the current legislative offense categories. One experienced researcher and four employees from a police force in the United Kingdom coded an extensive amount of IIOC (n = 1,212-2,233) to determine if they (a) were deemed to be of an indecent nature, and (b) depicted a child. Interrater reliability analyses revealed both considerable agreement and disagreement across coders, which were followed up with two focus groups involving the four employees. The first entailed a general discussion of the aspects that made such material more or less difficult to identify; the second focused around images where there had been either agreement (n = 20) or disagreement (n = 36) across coders that the images were of an indecent nature. Using thematic analysis, a number of factors apparent within IIOC were revealed to make the determination of youthfulness and indecency significantly more challenging for coders, with most relating to the developmental stage of the victim and the ambiguity of the context of an image. Findings are discussed in light of their implications for the identification of victims of ongoing sexual exploitation/abuse, the assessment and treatment of individuals in possession of IIOC, as well as the practice of policing and sentencing this type of offending behavior.

Simulation of facial growth based on longitudinal data: Age progression and age regression between 7 and 17 years of age using 3D surface data

Modelling of the development of facial morphology during childhood and adolescence is highly useful in forensic and biomedical practice. However, most studies in this area fail to capture the essence of the face as a three-dimensional structure. The main aims of our present study were (1) to construct ageing trajectories for the female and male face between 7 and 17 years of age and (2) to propose a three-dimensional age progression (age -regression) system focused on real growth-related facial changes. Our approach was based on an assessment of a total of 522 three-dimensional (3D) facial scans of Czech children (39 boys, 48 girls) that were longitudinally studied between the ages of 7 to 12 and 12 to 17 years. Facial surface scans were obtained using a Vectra-3D scanner and evaluated using geometric morphometric methods (CPD-DCA, PCA, Hotelling’s T² tests). We observed very similar growth rates between 7 and 10 years in both sexes, followed by an increase in growth velocity in both sexes, with maxima between 11 and 12 years in girls and 11 to 13 years in boys, which are connected with the different timing of the onset of puberty. Based on these partly different ageing trajectories for girls and boys, we simulated the effects of age progression (age regression) on facial scans. In girls, the mean error was 1.81 mm at 12 years and 1.7 mm at 17 years. In boys, the prediction system was slightly less successful: 2.0 mm at 12 years and 1.94 mm at 17 years. The areas with the greatest deviations between predicted and real facial morphology were not important for facial recognition. Changes of body mass index percentiles in children throughout the observation period had no significant influence on the accuracy of the age progression models for both sexes.

Child trafficking and the European migration crisis: The role of forensic practitioners

Trafficking in children is one of the worst forms of human rights violation and is categorised as a serious crime. Children at high risk of becoming victims of trafficking are runaways, children with a history of abuse, and migrant children. Internationally, cases of child trafficking are increasing the most in Europe, which is likely the result of the current migration crisis. In crises, preventing and combating human trafficking needs to be prioritized, considering that the aims of humanitarian action include saving lives, easing suffering and preserving human dignity. The involvement of forensic practitioners in investigations of cases of child trafficking mainly concerning the identification of victims may save lives and certainly alleviate suffering of the child victims and their families searching for them. Moreover, by aiding the prosecution process through thorough documentation and expert reporting forensic practitioners may contribute to the protection, rehabilitation and possibly compensation of the child victims, and thus to the restoration of their rights and dignity. So far, forensic practitioners were rarely specifically mentioned as actors in the counter-trafficking efforts in the multitude of policies, regulations, guidelines and recommendations concerning different aspects of child trafficking. This seems surprising considering that the expertise and experience of practitioners from forensic sciences including cyber forensics, document analysis, forensic biology, anthropology, and medicine can be utilised for gathering intelligence in cases of suspected human trafficking, for identifying the victims as well as perpetrators, and for securing evidence for legal proceedings as this paper shows. While this article mainly discusses the role of forensic pathologists and anthropologists, with a specific focus on the identification of child victims of trafficking in the context of the European migration crisis, the notions regarding the contribution of forensic sciences to the counter-trafficking efforts can be adapted to other geographical and sociopolitical contexts.

Modelling of facial growth in Czech children based on longitudinal data: Age progression from 12 to 15 years using 3D surface models

Dealing with the increasing number of long-term missing children and juveniles requires more precise and objective age progression techniques for the prediction of their current appearance. Our contribution includes detailed and real facial growth information used for modelling age progression during adolescence. This study was based on an evaluation of the overall 180 three-dimensional (3D) facial scans of Czech children (23 boys, 22 girls), which were longitudinally studied from 12 to 15 years of age and thus revealed the real growth-related changes. The boys underwent more marked changes compared with the girls, especially in the regions of the eyebrow ridges, nose and chin. Using modern geometric morphometric methods, together with their applications, we modelled the ageing and allometric trajectories for both sexes and simulated the age-progressed effects on facial scans. The facial parts that are important for facial recognition (eyes, nose, mouth and chin) all deviated less than 0.75mm, whereas the areas with the largest deviations were situated on the marginal parts of the face. The mean error between the predicted and real facial morphology obtained by modelling the children from 12 to 15 years of age was 1.92mm in girls and 1.86mm in boys. This study is beneficial for forensic artists as it reduces the subjectivity of age progression methods.

Three-dimensional analyses of aging-induced alterations in facial shape: a longitudinal study of 171 Japanese males

We sought to generate data to facilitate forensic facial comparisons. Specifically, we conducted a longitudinal study of alterations in face shape induced by aging. We obtained two three-dimensional facial shape measurements in 171 Japanese males at intervals of approximately 10 years. With this data, we created a homologous model consisting of 10,741 data points for each face based on 33 anatomical landmarks. We averaged the movements of corresponding data points between the two homologous models for each individual and used this data to predict up to 30 years of face aging in an average Japanese male. We clearly identified aging-induced shape changes, such as drooping and denting of the facial folds, drooping of the upper lip, and projection of the lower eyelid, in the virtually aged model. A quantitative comparison of aging-induced shape alterations among three age groups (individuals in their 20's, 30's, and 40-50's) showed that these alterations accelerated more quickly as age increased. Using our predictive model, we conducted a preliminary study focused on facial shape alterations induced by reductions in body weight. Our findings indicated that our proposed method would also be valid for this purpose.