Photo Dating by Facial Age Aggregation
This addresses the photo dating problem for archivists and historians, but it is incremental as it builds on existing face recognition and age estimation models.
The paper tackles the problem of estimating the year a photograph was taken by using facial information, and the result is a method that outperforms scene-based baselines, especially for images with multiple identifiable individuals.
We introduce a novel method for Photo Dating which estimates the year a photograph was taken by leveraging information from the faces of people present in the image. To facilitate this research, we publicly release CSFD-1.6M, a new dataset containing over 1.6 million annotated faces, primarily from movie stills, with identity and birth year annotations. Uniquely, our dataset provides annotations for multiple individuals within a single image, enabling the study of multi-face information aggregation. We propose a probabilistic framework that formally combines visual evidence from modern face recognition and age estimation models, and career-based temporal priors to infer the photo capture year. Our experiments demonstrate that aggregating evidence from multiple faces consistently improves the performance and the approach significantly outperforms strong, scene-based baselines, particularly for images containing several identifiable individuals.