Finding Selfies of Users in Microblogged Photos

Abstract

We examine the use of clustering to identify selfies in a social media user’s photos for use in estimating demographic information such as age, gender, and race. 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.