Applicability of a pre-established set of facial proportions from frontal photographs in forensic age estimation of a Brazilian population

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.

Facial morphological analysis for the development of a representative atlas of the brazilian population

The objective of this study was to propose categories of morphological classification for the face and its anatomical structures, as well as to propose illustrations to support the development of an atlas that facilitates facial morphological analysis of adult Brazilians. It was a descriptive study based on the analysis of the frequency and distribution of 13 photoanthropometric facial ratios (RFAs) obtained from a representative sample of the Brazilian population. RFAs related to facial height and width, eye width, intercanthal distance, nose length and width, philtrum ridge height and width, mouth thickness and width, upper and lower lip thickness, and chin height were analyzed. The study included a sample of 5.000 individuals aged between 18 and 22 years, evenly distributed between genders. Data normality was assessed using the Shapiro-Wilk test, considering them as parametric when p > 0.05. For the RFAs that showed normal distribution, mean ± 1.5 standard deviations (SD) were used to categorize facial measurements as regular, below average, or above average. Non-parametric RFAs were analyzed based on the median and 10th and 90th percentiles of the data. Based on the established average iris diameter, which is considered the most stable facial measurement, the values of the described RFAs were converted to a numerical scale in centimeters, allowing for the illustration of female and male faces. In this way, it was possible to categorize the facial anatomical structures and, consequently, visualize the facial morphological pattern of the adult Brazilian population.

Is facial geometric morphometrics a useful method for age estimation in children and adolescents? Limited evidence and lack of studies leave us with an uncertain answer

Facial geometric morphometrics is a non-invasive method that has recently shown potential applications, including age estimation, diagnosis of facial abnormalities, monitoring facial development, and evaluating treatment outcomes. A systematic review identified two studies that demonstrated the use of facial geometric morphometrics for age estimation in children and adolescents, showing promising results in terms of accuracy and error. This finding could be particularly relevant in forensic investigations. However, a research agenda should be established to prioritize the assessment of the diagnostic accuracy of facial morphometric geometrics in estimating age among children and adolescents.

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.

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.