Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Human Identity from Motion Patterns   We present a large-scale study exploring the capability of temporal deep
neural networks to interpret natural human kinematics and introduce the first
method for active biometric authentication with mobile inertial sensors. At
Google, we have created a first-of-its-kind dataset of human movements,
passively collected by 1500 volunteers using their smartphones daily over
several months. We (1) compare several neural architectures for efficient
learning of temporal multi-modal data representations, (2) propose an optimized
shift-invariant dense convolutional mechanism (DCWRNN), and (3) incorporate the
discriminatively-trained dynamic features in a probabilistic generative
framework taking into account temporal characteristics. Our results demonstrate
that human kinematics convey important information about user identity and can
serve as a valuable component of multi-modal authentication systems.

11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th