  Person identification in the wild is very challenging due to great variation
in poses, face quality, clothes, makeup and so on. Traditional research, such
as face recognition, person re-identification, and speaker recognition, often
focuses on a single modal of information, which is inadequate to handle all the
situations in practice. Multi-modal person identification is a more promising
way that we can jointly utilize face, head, body, audio features, and so on. In
this paper, we introduce iQIYI-VID, the largest video dataset for multi-modal
person identification. It is composed of 600K video clips of 5,000 celebrities.
These video clips are extracted from 400K hours of online videos of various
types, ranging from movies, variety shows, TV series, to news broadcasting. All
video clips pass through a careful human annotation process, and the error rate
of labels is lower than 0.2\%. We evaluated the state-of-art models of face
recognition, person re-identification, and speaker recognition on the iQIYI-VID
dataset. Experimental results show that these models are still far from being
perfect for the task of person identification in the wild. We proposed a
Multi-modal Attention module to fuse multi-modal features that can improve
person identification considerably. We have released the dataset online to
promote multi-modal person identification research.
