A Century of Portraits: A Visual Historical Record of American High School Yearbooks
This work addresses the challenge for historians and researchers in manually analyzing visual historical records at scale, though it is incremental in applying existing techniques to a new domain.
The paper tackled the problem of analyzing large-scale historical image datasets by creating a dataset of 168,055 American high school yearbook portraits and developing methods to discover visual trends and predict portrait dates, achieving a median error of 4 years for women and 6 for men.
Imagery offers a rich description of our world and communicates a volume and type of information that cannot be captured by text alone. Since the invention of the camera, an ever-increasing number of photographs document our "visual culture" complementing historical texts. But currently, this treasure trove of knowledge can only be analyzed manually by historians, and only at small scale. In this paper we perform automated analysis on a large-scale historical image dataset. Our main contributions are: 1) A publicly-available dataset of 168,055 (37,921 frontal-facing) American high school yearbook portraits. 2) Weakly-supervised data-driven techniques to discover historical visual trends in fashion and identify date-specific visual patterns. 3) A classifier to predict when a portrait was taken, with median error of 4 years for women and 6 for men. 4) A new method for discovering and displaying the visual elements used by the CNN-based date-prediction model to date portraits, finding that they correspond to the tell-tale fashions of each era. Project page can be found at: http://people.eecs.berkeley.edu/~shiry/projects/yearbooks/yearbooks.html .