Performance evaluation of face alignment algorithms on "in-the-wild" selfies

Abstract

Recently mobile apps, which beautify human face or apply cute masks to a human face, become very popular and gain lots of attention in media. These tasks require very precise landmarks localization to avoid "uncanny valley" effect. We introduce the new dataset of selfies, that were taken on mobile devices, and robustly evaluate and compare different state-of-the-art approaches to the task of face alignment. Evidently, our dataset allows to reliably rank face alignment algorithms that is superior to the most popular dataset in that area of research.