To Recognize Families In the Wild: A Machine Vision Tutorial

Abstract

Automatic kinship recognition has relevance in an abundance of applications. For starters, aiding forensic investigations, as kinship is a powerful cue that could narrow the search space (e.g., knowledge that the 'Boston Bombers' were brothers could have helped identify the suspects sooner). In short, there are many beneficiaries that could result from such technologies: whether the consumer (e.g., automatic photo library management), scholar (e.g., historic lineage & genealogical studies), data analyzer (e.g., social-media- based analysis), investigator (e.g., cases of missing children and human trafficking. For instance, it is unlikely that a missing child found online would be in any database, however, more than likely a family member would be), or even refugees. Besides application- based problems, and as already hinted, kinship is a powerful cue that could serve as a face attribute capable of greatly reducing the search space in more general face-recognition problems. In this tutorial, we will introduce the background information, progress leading us up to these points, several current state-of-the-art algorithms spanning various views of the kinship recognition problem (e.g., verification, classification, tri-subject). We will then cover our large-scale Families In the Wild (FIW) image collection, several challenge competitions it as been used in, along with the top per- forming deep learning approaches. The tutorial will end with a discussion about future research directions and practical use-cases.