Facial Recognition for Stolen Artifacts

As part of her master’s thesis at Boston University, archaeology student Hallie Baker is developing a machine learning system to help identify looted Cambodian artifacts held in museum collections. Beginning in the 1960s during the country’s civil war and continuing into the early 2000s, Cambodia was heavily targeted for cultural looting. This looting was driven by Western museums and collectors, and today the Cambodian government is proactively pursuing repatriations from such institutions as the Metropolitan Museum of Art. Currently, researchers must manually search through thousands of archival photographs to find a match to a statue on display in a museum today—a slow and labor-intensive process. Yet associating these images is very important: the photos can raise red flags about the legality of an object or aid in the repatriation process by demonstrating proof of Cambodia’s ownership. Hallie is working to automate the matching of these images by building a searchable image database (currently containing over 600 images and 209 verified matches) and training a CNN model. Her project aims to eventually power a public-facing website where users can upload an image and receive potential matches. Her thesis lays out the need for this infrastructure, the technical steps required to build it, and future plans to expand the project to Indian and Nepali artifacts. The project is designed to be open-source and ultimately contribute to global heritage preservation.