  Person re-identification aims to identify a person from an image collection,
given one image of that person as the query. There is, however, a plethora of
real-life scenarios where we may not have a priori library of query images and
therefore must rely on information from other modalities. In this paper, an
attribute-based approach is proposed where the person of interest (POI) is
described by a set of visual attributes, which are used to perform the search.
We compare multiple algorithms and analyze how the quality of attributes
impacts the performance. While prior work mostly relies on high precision
attributes annotated by experts, we conduct a human-subject study and reveal
that certain visual attributes could not be consistently described by human
observers, making them less reliable in real applications. A key conclusion is
that the performance achieved by non-expert attributes, instead of
expert-annotated ones, is a more faithful indicator of the status quo of
attribute-based approaches for person re-identification.
