History Professor Shawn Graham, along with three of his colleagues, have published a paper in the Journal of Computer Applications in Archaeology (CAA). The full article, “Towards a Method for Discerning Sources of Supply within the Human Remains Trade via Patterns of Visual Dissimilarity and Computer Vision” by Professors Shawn Graham (History Department), Alex Lane (Carleton University), Damien Huffer (History Department), and Andreas Angourakis (University of Cambridge) is available online with the abstract included below.
Abstract
While traders of human remains on Instagram will give some indication, their best estimate, or repeat hearsay, regarding the geographic origin or provenance of the remains, how can we assess the veracity of these claims when we cannot physically examine the remains? A novel image analysis using convolutional neural networks in a one-shot learning architecture with a triplet loss function is used to develop a range of ‘distances’ to known ‘reference’ images for a group of skulls with known provenances and a group of images of skulls from social media posts. Comparing the two groups enables us to predict a broad geographic ‘ancestry’ for any given skull depicted, using a mixture discriminant analysis, as well as a machine-learning model, on the image dissimilarity scores. It thus seems possible to assign, in broad strokes, that a particular skull has a particular geographic ancestry.