Age-at-death estimation from 3D bone models of the pubic symphysis by using a deep multi-view learning approach

Abstract

Forensic anthropology is a sub-field of physical anthropology that analyzes human skeletal remains of medical and legal interest. Within it, estimating an individual’s biological profile plays a crucial role for forensic anthropologists and pathologists in identifying victims and documenting evidence. In particular, the estimation of the age-at-death of an individual is crucial to reduce the range of possible coincidences during the human identification process. Most existing approaches rely on the forensic expert’s subjective evaluation and do not consider relevant characteristics that are not detectable by traditional visual methods. This paper proposes a deep multiview learning approach to automatically estimate an individual’s age-at-death from three 2D panoramic views generated from a 3D bone model of their pubic symphysis. These three images correspond to the three principal coordinate axes and consist of 3-channel images generated from the spatial distribution map, normals’ deviation map, and gradient normals’ deviation map projections. Our approach not only detects high-level characteristics of the pubic symphysis undetectable by the human eye, but also reduces the age estimation error to 6.74, becoming, as far as we know, the best result in the state-of-the-art. In addition, the proposed approach includes a post-hoc explanation stage that visualizes, through a 3D saliency map of the pubic symphysis, the most relevant pixel areas on which the proposed neural architecture relies to make a given decision. This provides forensic scientists with valuable information and confidence to support their decision-making process.