  Many mobile systems and wearable devices, such as Virtual Reality (VR) or
Augmented Reality (AR) headsets, lack a keyboard or touchscreen to type an ID
and password for signing into a virtual website. However, they are usually
equipped with gesture capture interfaces to allow the user to interact with the
system directly with hand gestures. Although gesture-based authentication has
been well-studied, less attention is paid to the gesture-based user
identification problem, which is essentially an input method of account ID and
an efficient searching and indexing method of a database of gesture signals. In
this paper, we propose FMHash (i.e., Finger Motion Hash), a user identification
framework that can generate a compact binary hash code from a piece of
in-air-handwriting of an ID string. This hash code enables indexing and fast
search of a large account database using the in-air-handwriting by a hash
table. To demonstrate the effectiveness of the framework, we implemented a
prototype and achieved >99.5% precision and >92.6% recall with exact hash code
match on a dataset of 200 accounts collected by us. The ability of hashing
in-air-handwriting pattern to binary code can be used to achieve convenient
sign-in and sign-up with in-air-handwriting gesture ID on future mobile and
wearable systems connected to the Internet.
