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      Estimating sex and age for forensic applications using machine learning based on facial measurements from frontal cephalometric landmarks

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          Abstract

          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 by using a group of cephalometric landmarks to estimate anthropological information. 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, to create an artificial neural network classifier that allows the estimation of anthropological information, in this specific case age and sex. The work is focused on four tasks: i) sex estimation over 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 classifier obtained significant results, using this new data set, for the sex estimation of individuals over 14 years old, achieving accuracy values greater than 0.85 by the F_1 measure. For age estimation, the accuracy results are 0.72 for measure with an age interval of 5 years. For the age group estimation, the measures of accuracy are greater than 0.93 and 0.83 for thresholds of 14 and 18 years, respectively.

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          A standardized nomenclature for craniofacial and facial anthropometry

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            Can facial proportions taken from images be of use for ageing in cases of suspected child pornography? A pilot study.

            The age of the victim plays a crucial role for the legal implications concerning pornography. Judges therefore often call on forensic experts to verify the age of individuals depicted on photographs or videos. However, there is no scientifically established protocol available for forensic practice in such cases. The conventional methods such as the evaluation of secondary sexual characteristics provide unsatisfactory results particularly when the legally relevant ages for child pornography (i.e. 14 and 18 years) are concerned. To overcome these limits, a European research group has explored the applicability of facial proportions as an age indicator on images. In this pilot study, standardized facial images of 353 females and 20 males from four age groups (6, 10, 14 and 18 years) were randomly selected for the metric analysis from a large data set including German, Italian and Lithuanian subjects. In this sample, several indices extracted from the frontal and lateral photographs were closely correlated to their respective indices taken from the living individuals. Furthermore, age-related changes were identified for indices taken from the photographs. The discriminant analysis showed that for the pooled sample, 60.3% of the cases were correctly classified into the respective age group. The percentage of correctly classified cases increased in the respective country samples as follows: 69.9% for Germany, 69.4% for Lithuania and 80.5% for Italy. The present study suggests that the metric assessment of the face may be used for age estimation on images. Nonetheless, more work needs to be done in order to verify the reliability of these findings on a large sample.
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              A new approach for the analysis of facial growth and age estimation: Iris ratio

              The study of facial growth is explored in many fields of science, including anatomy, genetics, and forensics. In the field of forensics, it acts as a valuable tool for combating child pornography. The present research proposes a new method, based on relative measurements and fixed references of the human face—specifically considering measurements of the diameter of the iris (iris ratio)—for the analysis of facial growth in association with age in children and sub-adults. The experimental sample consisted of digital photographs of 1000 Brazilian subjects, aged between 6 and 22 years, distributed equally by sex and divided into five specific age groups (6, 10, 14, 18, and 22 year olds ± one month). The software package SAFF-2D® (Forensic Facial Analysis System, Brazilian Federal Police, Brazil) was used for positioning 11 landmarks on the images. Ten measurements were calculated and used as fixed references to evaluate the growth of the other measurements for each age group, as well the accumulated growth (6–22 years old). The Intraclass Correlation Coefficient (ICC) was applied for the evaluation of intra-examiner and inter-examiner reliability within a specific set of images. Pearson’s Correlation Coefficient was used to assess the association between each measurement taken and the respective age groups. ANOVA and Post-hoc Tukey tests were used to search for statistical differences between the age groups. The outcomes indicated that facial structures grow with different timing in children and adolescents. Moreover, the growth allometry expressed in this study may be used to understand what structures have more or less proportional variation in function for the age ranges studied. The diameter of the iris was found to be the most stable measurement compared to the others and represented the best cephalometric measurement as a fixed reference for facial growth ratios (or indices). The method described shows promising potential for forensic applications, especially as part of the armamentarium against crimes involving child pornography and child abuse.
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                Author and article information

                Journal
                06 August 2019
                Article
                1908.02353
                e51fff77-617f-4a7c-8b99-266903d7367d

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                17 pages, 17 figures
                cs.CV cs.LG

                Computer vision & Pattern recognition,Artificial intelligence
                Computer vision & Pattern recognition, Artificial intelligence

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