  In this paper, we demonstrate the benefits of using state-of-the-art machine
learning methods in the analysis of historical photo archives. Specifically, we
analyze prominent Finnish World War II photographers, who have captured high
numbers of photographs in the publicly available Finnish Wartime Photograph
Archive, which contains 160,000 photographs from Finnish Winter, Continuation,
and Lapland Wars captures in 1939-1945. We were able to find some special
characteristics for different photographers in terms of their typical photo
content and framing (e.g., close-ups vs. overall shots, number of people).
Furthermore, we managed to train a neural network that can successfully
recognize the photographer from some of the photos, which shows that such
photos are indeed characteristic for certain photographers. We further analyzed
the similarities and differences between the photographers using the features
extracted from the photographer classifier network. We make our annotations and
analysis pipeline publicly available, in an effort to introduce this new
research problem to the machine learning and computer vision communities and
facilitate future research in historical and societal studies over the photo
archives.
