## 3 Forensic Human Identification through the Analysis of Biomedical Images  
### AI-Based Approaches for Biological Profile Estimation  
The most common scenario in FA is to have direct access to the bones; bones that are extracted, cleaned and manipulated by the forensic anthropologist. However, radiological imaging, mainly CT scanning, is gaining popularity for BP since it is a non-invasive approach (in the sense that the human expert do not need to have direct visual access to the bone), and it allows better possibilities for observation and metric calculations. Estimation of BP from X-ray images has been approached from three different scientific communities with three different purposes. In clinical medicine, the biological age is important when determining endocrinological diseases in adolescents or for optimally planning the time-point of paediatric orthopaedic surgery interventions. Nevertheless, in legal medicine, is used to approximate unknown chronological age, when determining age in cases of criminal investigations or for asylum-seeking procedures, where identification documents of children or adolescents are missing. Finally, physical and forensic anthropologists are interested in determining sex, age, stature and ancestry of any human remain.  
The bigger international efforts have been focused on the development of accurate and objective automatic approaches for assessing whether living individuals have reached the threshold age that implies legal adulthood. There are different computer-based proposals to assist the assessment procedure by using radiological imaging methods. Usually, MRI or X-ray images of the hand/wrist are employed in some AI-based proposals using SC techniques such as fuzzy decision trees [59], random forests [60], or neural networks [61]. Recently, the proliferation of convolutional neural networks (ConvNets) has attracted interest in medical image analysis. DL techniques overcome many of these limitations by allowing to automatically learn the suitable features for image interpretation without any direct human intervention during the training process.  
Nevertheless, to our knowledge, there is no automatic approach facing a complete forensic BP assessment considering different stages and a whole age range. Most published automatic methods operate only with X-ray scans of Caucasian subjects younger than 10 years, with a few approaches dealing with subjects less than 18 years old. The automation of age estimation in adults is much less technologically developed.  
The following subsections will focus on sex and age estimation. Tables 1 and 2 include an overview of the main AI-based approaches employed in the literature for sex and age estimation, respectively. There are some cases (e.g., [PERSON] et al. [62], [PERSON] et al. [63] or [PERSON] et al. [64; 65; 66]) that, probably, fit more the image/signal processing domain than the AI research field. However, we consider those works relevant and related at a computational level and, thus, they are also mentioned in the corresponding table or subsection. It is also important to highlight that there are other remarkable approximations (like [67]) that, despite their use of advanced intelligent systems, are not tackled in this paper, since they do not actually use biomedical images (in the case of [67], the author uses ConvNets to estimate the sex of individuals by detecting biometric tracts in photographs of hands). Finally, it is worth mentioning that there are also previous studies using AI-based approaches in BP estimation, like ML for sex estimation [68; 69], but these employ measurements manually taken by forensic anthropologists and not images, so again they are not included in this survey.