  Human identification is an important topic in event detection, person
tracking, and public security. There have been numerous methods proposed for
human identification, such as face identification, person re-identification,
and gait identification. Typically, existing methods predominantly classify a
queried image to a specific identity in an image gallery set (I2I). This is
seriously limited for the scenario where only a textual description of the
query or an attribute gallery set is available in a wide range of video
surveillance applications (A2I or I2A). However, very few efforts have been
devoted towards modality-free identification, i.e., identifying a query in a
gallery set in a scalable way. In this work, we take an initial attempt, and
formulate such a novel Modality-Free Human Identification (named MFHI) task as
a generic zero-shot learning model in a scalable way. Meanwhile, it is capable
of bridging the visual and semantic modalities by learning a discriminative
prototype of each identity. In addition, the semantics-guided spatial attention
is enforced on visual modality to obtain representations with both high global
category-level and local attribute-level discrimination. Finally, we design and
conduct an extensive group of experiments on two common challenging
identification tasks, including face identification and person
re-identification, demonstrating that our method outperforms a wide variety of
state-of-the-art methods on modality-free human identification.
