  The past few years have witnessed renewed interest in NLP tasks at the
interface between vision and language. One intensively-studied problem is that
of automatically generating text from images. In this paper, we extend this
problem to the more specific domain of face description. Unlike scene
descriptions, face descriptions are more fine-grained and rely on attributes
extracted from the image, rather than objects and relations. Given that no data
exists for this task, we present an ongoing crowdsourcing study to collect a
corpus of descriptions of face images taken `in the wild'. To gain a better
understanding of the variation we find in face description and the possible
issues that this may raise, we also conducted an annotation study on a subset
of the corpus. Primarily, we found descriptions to refer to a mixture of
attributes, not only physical, but also emotional and inferential, which is
bound to create further challenges for current image-to-text methods.
