Automatically Selecting the Best Pictures for an Individualized Child Photo Album

Abstract

In this paper we investigate the best way to automatically compose a photo album for an individual child from a large collection of photographs taken during a school year. For this, we efficiently combine state-of-the-art identification algorithms to select relevant photos, with an aesthetics estimation algorithm to only keep the best images. For the identification task, we achieved $86\%$ precision for $86\%$ recall on a real-life dataset containing lots of specific challenges of this application. Indeed, playing children appear in non-standard poses and facial expressions, can be dressed up or have their faces painted etc. In a top-1 sense, our system was able to correctly identify $89.2\%$ of the faces in close-up. Apart from facial recognition, we discuss and evaluate extending the identification system with person re-identification. To select out the best-looking photos from the identified child photos to fill the album with, we propose an automatic assessment technique that takes into account the aesthetic photo quality as well as the emotions in the photos. Our experiments show that this measure correlates well with a manually labeled general appreciation score.