## 3 Forensic Human Identification through the Analysis of Biomedical Images
### AI-Based Approaches for Craniofacial Superimposition
#### 3.4.3 Skull-Face Overlay Assessment and Decision Making  
The forensic expert has to make a decision to determine the degree of support that the skull and facial photographs belong to the same person or not. This decision is made through the analysis of the previous SFOs and influenced by several criteria assessing the skull-face anatomical correspondence. This way, different authors have defined and classified those criteria in four different families [168, 189,190]: (1) Analysis of the consistency of the bony and facial outlines/morphological curves; (2) Assessment of the anatomical consistency by positional relationship; (3) Line location and comparison to analyze anatomical consistency; and (4) Evaluation of the consistency of the soft tissue thickness between corresponding cranial and facial landmarks. This is a subjective process that relies on the forensic expert's skills and the quantity and quality of the used materials. There are just a few works tackling the automation of the analysis of craniofacial correspondences within the framework of CFS ID [168,191,192]. Most of the existing literature was published more  
Figure 7: Diagram showing the approximation followed in [173,174,177]. An evolutionary algorithm (EA) iteratively guides the search for the transformation parameters of an image registration (IR) process. The goal is to get the best possible skull-face overlay (SFO) for craniofacial identification. More specifically, the forensic expert marks fuzzy landmarks on the facial image and then marks crisp/precise landmarks on the skull (3D model that can be rotated and manipulated with ease and precision). Those crisp landmarks are fuzzified taking into account facial soft tissue depth population studies. Finally, these 3D fuzzy landmarks are projected onto the 2D image, overlap measurements between two fuzzy sets are calculated, and all this information is integrated into the EA fitness function. than 20 years ago and the works are very basic and limited. In addition, they do not consider the use of either skull 3D models or computer techniques to perform the SFO. Besides, the employed technique for the shape analysis implies manual interaction. They provide a value that does not take into account the actual spatial relation between skull and face since the employed methods are invariant to translation, scale and rotation. Finally, these systems only implement a single group of the criteria to assess the craniofacial correspondence. Recently, in [178; 183; 179], the authors present a hierarchical system to evaluate the anatomical consistence of morphological criteria between the face and the skull and give support to the forensic expert decision-making process. From a series of SFOs of the same individual, the computer-assisted decision support system (CADSS) provides to the forensic expert a quantitative output value that is indicative of the morphological matching consistency of a given CFS problem. This quantitative value is based on the use of several skull-face anatomical criteria combined at different levels by means of fuzzy aggregation functions considering the evaluation of the anatomical correspondence between the skull and the face at three different levels: criterion evaluation (level 3), SFO evaluation (level 2), and CFS evaluation (level 1). The sources of uncertainty and degrees of confidence involved in the process (bone preservation and 3D model quality, image quality, discriminatory power of each individual criterion, BP influence) were modeled and considered at each level of the system [183]. With the aim of comparing the performance of real forensic practitioners with this CFS CADSS, the authors applied their CADSS to the same experimental dataset of [193]. In that study, 26 participants from 17 different institutions were asked to deal with 14 identification scenarios, some of them involving the comparison of multiple candidates and unknown skulls. A total number of 60 SFO problems were tackled.