Towards a Method for Discerning Sources of Supply within the Human Remains Trade via Patterns of Visual Dissimilarity and Computer Vision. Issue 1 (25th August 2020)

• Record Type: Journal Article
• Authors: Graham, Shawn; Lane, Alex; Huffer, Damien; Angourakis, Andreas
• Is Part Of: Journal of computer applications in archaeology. Volume 3:Issue 1(2020)
• Page Start: 253
• Page End: 268
• Publication Date: 2020-08-25
• Subjects: human remains -- social media -- neural networks -- computer vision -- antiquities trade -- illicit; 930.10285
• DOI: 10.5334/jcaa.59
• Languages: English
• ISSNs: 2514-8362

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.