## 3 Forensic Human Identification through the Analysis of Biomedical Images
### AI-Based Approaches for Biological Profile Estimation
#### 3.1.1 Sex Estimation from Skeletal Structures  
Sex estimation is a fundamental pillar of the BP. If sex estimation is incorrect, then the identification may be delayed. Current methods are mainly based on morphometric or morphological criteria [70; 71]. The morphometric approach involves the measurements of hands, feet, and the extremities corresponding to the upper, lower and long bones. On the other hand, the morphological approach finds its foundation in the sexual dimorphism, presented in certain degrees in most of the bones in human body. In particular, the most common and popular parts of human body for sex estimation are skull and the pelvis area [62]. However, these methods are subject to human biases, require a high degree of expertise, are complex, time-consuming [72], and are not always suitable, mainly due to the presence of a significant damage (chemical and/or physical) on the skeletal remains. AI techniques, especially DL approaches, offer a flexible and powerful solution to the sex estimation from skeletal structures. Some researchers have tried different DL techniques for sex estimation [62; 63; 73; 74], mainly focusing on adults, since the adult's body is mature enough to point out significant clues which can help to distinguish its sex, but may not be suitable for sex estimation in children [75]. Table 1 offers an overview of the main AI-based approaches employed in the literature for sex estimation. [PERSON] et al. [73] used a Hybrid Particle Swarm Artificial Neural Network technique for sex estimation of individuals. They used a dataset of left hand X-ray images of Asian population. Their data set was small and their results suggest a different accuracy for different age groups. [PERSON] et al. [62] introduced a methodology for objective quantification of sexually dimorphic features on images of skull and pelvis using the Wavelet Transform, as a multi-scale mathematical tool that allows for measurements of shape variations that are hidden at different scales of resolution. This information can be used by experts to improve the accuracy of BP assessment, and to describe the geographic \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Methods** & **Data** \\ \hline [PERSON] et al., 2007 [76] & 3D Statistical Shape Models, Fuzzy C-Means \& Linear & 228 patella CT scans \\  & Discriminant Classification & \\ [PERSON] et al., 2015 [73] & PSO \& Neural Network & 333 hand-wrist radiographs \\ [PERSON] et al., 2016 [62] & Wavelet transform \& 19 3D point clouds & 19 3D point clouds \\  & shape analysis & of the skull supraorbital margin \\ [PERSON] et al., 2017 [63] & Image processing tools & 39 Haverisan canal microscopic images \\ [PERSON] \& [PERSON], 2018 [75] & ConvNet & 12,614 hand-wrist radiographs \\ [PERSON] et al., 2019 [74] & ConvNet & 1000 skull CT scans \\ [PERSON] et al., 2019 [77] & Artificial Neural Network & 267 skull CT scans \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An overview of the literature on artificial intelligence (AI)-based approaches for sex estimation from skeletal data. References are ordered according to their publication date. and temporal variations within and among populations. In [63], the authors presented an automated Haversian canal detection system based on the histomorphology, which uses only bone fragments to estimate age and sex. They divided their detection system into two parts. In the first part, they manually analyzed and observed differences in the parameters of male and female sample of bones. In the second part, they applied microstructural image processing techniques to identify the gender. [PERSON] et al. [74] addressed the problem of sex estimation of skeletal remains by training a ConvNet with images of 900 skulls virtually reconstructed from hospital CT scans. When tested on previously unseen images of skulls, the deep network showed 95% accuracy at sex estimation.