Assessing Photographer Competence Using Face Statistics

The rapid growth of photo sharing websites has resulted in some new problems around the management of a large (and quickly increasing) number of photographers with different needs and usage characteristics. Despite significant advances in the field of computer vision, little has been done to leverage these technologies for photographer understanding and management, partly due to the high computational cost of extracting application-specific image features. Recently robust multi-view face detection technologies have been widely adopted by many photo sharing sites. This affords a limited but "standard" pre-computed set of face features to tackle these administrative problems in large scale settings. In this paper we present a principled statistical model to alleviate one such administrative task - the automatic analysis of photographer competency given only face detection results on a set of their photos. The model uses summary statistics to estimate the probability a given individual belongs to a population of high competence photographers over against a second population of lower competence photographers. Using this model, we have achieved high classification accuracy (respectively 84.3% and 90.9%) on two large image datasets. We discuss an application of this approach to assist in managing a photo-sharing website.