A Century of Portraits: A Visual Historical Record of American High School Yearbooks
Shiry Ginosar, Kate Rakelly, Sarah Sachs, Brian Yin, Crystal Lee,, Philipp Krahenbuhl, Alexei A. Efros

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…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis
