Excavations at Aberdeen's Carmelite Friary, 1980-1994

Cameron, Alison; Stones, Judith; Croly, Chris

doi:10.11141/ia.52.1

Cited by 1 publication

(1 citation statement)

References 48 publications

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“…In other research we showed that there were ethical and technological problems with using neural networks to classify these images of human remains, or in using transfer-learning techniques which require thousands of images of a particular classification in order to work (Huffer & Graham 2018;Huffer, Wood & Graham 2019). It is unfeasible and impractical to try to create such training data in the domain of human remains.…”

Section: The Trade In Human Remains On Social Media and E-commerce Plmentioning

confidence: 99%

Towards a Method for Discerning Sources of Supply within the Human Remains Trade via Patterns of Visual Dissimilarity and Computer Vision

Graham

Lane

Huffer

et al. 2020

Journal of Computer Applications in Archaeology

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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.

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Section: The Trade In Human Remains On Social Media and E-commerce Plmentioning

confidence: 99%