Finding selfies of users in microblogged photos

Abstract

We examine the use of clustering to identify selfies in a social media user's photos. Faces are first detected within a user's photos followed by clustering using visual similarity. We define a cluster scoring scheme that uses a combination of within-cluster visual similarity and average face size in a cluster to rank potential selfie-clusters. Finally, we evaluate this ranking approach over a collection of Twitter users and discuss methods that can be used for improving performance in the future. An application of user selfies is estimating demographic information such as age, gender, and race in a more robust fashion.