A Century of Portraits: A Visual Historical Record of American High School Yearbooks

TL;DR

This paper introduces a large-scale dataset of American high school yearbook portraits and develops automated methods to analyze visual trends, predict portrait dates, and interpret fashion cues over a century.

Contribution

It provides a publicly available dataset and novel weakly-supervised techniques for analyzing historical visual trends and dating portraits with high accuracy.

Findings

Median error of 4 years for women and 6 for men in date prediction

Identified era-specific fashion patterns using CNN explanations

Demonstrated the potential for large-scale automated analysis of historical images

Abstract

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 .