During the prosecution process the primary objective is to prove criminal offences to the correct perpetrator to convict them with legal effect. However, in reality this may often be difficult to achieve. Suppose a suspect has been identified and is accused of a bank robbery. Due to the location of the crime, it can be assumed that there is sufficient image and video surveillance footage available, having recorded the perpetrator at the crime scene. Depending on the surveillance system used, there could be even high-resolution material available. In short, optimal conditions seem to be in place for further investigations, especially as far as the identification of the perpetrator and the collection of evidence of their participation in the crime are concerned. However, perpetrators usually act using some kind of concealment to hide their identity. In most cases, they disguise their faces and even their gait. Conventional investigation approaches and methods such as facial recognition and gait analysis then quickly reach their limits. For this reason, an approach based on anthropometric person-specific digital skeletons, so-called rigs, that is being researched by the COMBI research project is presented in this publication. Using these rigs, it should be possible to assign known identities, comparable to suspects, to unknown identities, comparable to perpetrators. The aim of the COMBI research project is to study the anthropometric pattern as a biometric identifier as well as to make it feasible for the standardised application in the taking of evidence by the police and prosecution. The approach is intended to present computer-aided opportunities for the identification of perpetrators that can support already established procedures.

Forensic identification of individuals faces significant challenges, particularly when conventional biometric features such as the face are hidden. This paper examines the historical development and revival of body patterns (anthropometric rig) as biometric comparison feature, from historical Bertillonage to modern, computer-assisted methods such as digital anthropometric rig matching and the connection to 3D human pose estimation (HPE). It highlights both the mathematical and methodological foundations of this revival, as well as the potential and limitations of applying artificial intelligence (AI) in the context of digital anthropometric rig matching. The aim is to highlight the development of potential and challenges for the forensic validity of the person-specific digital skeleton. This clearly shows the time required for manual work, which underlines the need for automation. The time required can be reduced by approaches that use AI. However, these methods are often not yet up to the requirements in a forensic context.