# Machine Learning Based Analysis of Finnish World War II Photographers

## TL;DR

This paper applies advanced machine learning techniques to analyze Finnish WWII photographers, revealing unique photographic styles and enabling automatic photographer recognition, thus opening new avenues for historical and societal research.

## Contribution

It introduces a novel machine learning approach to analyze historical photo archives, including a neural network for photographer recognition and analysis pipeline, with publicly available annotations.

## Key findings

- Neural network successfully recognizes photographers from photos.
- Distinct photographic styles identified for different photographers.
- Analysis pipeline and annotations made publicly available.

## Abstract

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.