  Biometric identification systems have become immensely popular and important
because of their high reliability and efficiency. However person identification
at a distance, still remains a challenging problem. Gait can be seen as an
essential biometric feature for human recognition and identification. It can be
easily acquired from a distance and does not require any user cooperation thus
making it suitable for surveillance. But the task of recognizing an individual
using gait can be adversely affected by varying view points making this task
more and more challenging. Our proposed approach tackles this problem by
identifying spatio-temporal features and performing extensive experimentation
and training mechanism. In this paper, we propose a 3-D Convolution Deep Neural
Network for person identification using gait under multiple view. It is a
2-stage network, in which we have a classification network that initially
identifies the viewing point angle. After that another set of networks (one for
each angle) has been trained to identify the person under a particular viewing
angle. We have tested this network over CASIA-B publicly available database and
have achieved state-of-the-art results. The proposed system is much more
efficient in terms of time and space and performing better for almost all
angles.
