Detecting Faces, Visual Medium Types, and Gender in Historical Advertisements, 1950-1995

Abstract

Libraries, museums, and other heritage institutions are digitizing large parts of their archives. Computer vision techniques enable scholars to query, analyze, and enrich the visual sources in these archives. However, it remains unclear how well algorithms trained on modern photographs perform on historical material. This study evaluates and adapts existing algorithms. We show that we can detect faces, visual media types, and gender with high accuracy in historical advertisements. It remains difficult to detect gender when faces are either of low quality or relatively small or large. Further optimization of scaling might solve the latter issue, while the former might be ameliorated using upscaling. We show how computer vision can produce meta-data information, which can enrich historical collections. This information can be used for further analysis of the historical representation of gender.