  Gait of a person refers to his/her walking pattern, and according to medical
studies gait of every individual is unique. Over the past decade, several
computer vision-based gait recognition approaches have been proposed in which
walking information corresponding to a complete gait cycle has been used to
construct gait features for person identification. These methods compute gait
features with the inherent assumption that a complete gait cycle is always
available. However, in most public places occlusion is an inevitable
occurrence, and due to this, only a fraction of a gait cycle gets captured by
the monitoring camera. Unavailability of complete gait cycle information
drastically affects the accuracy of the extracted features, and till date, only
a few occlusion handling strategies to gait recognition have been proposed. But
none of these performs reliably and robustly in the presence of a single cycle
with incomplete information, and because of this practical application of gait
recognition is quite limited. In this work, we develop deep learning-based
algorithm to accurately identify the affected frames as well as predict the
missing frames to reconstruct a complete gait cycle. While occlusion detection
has been carried out by employing a VGG-16 model, the model for frame
reconstruction is based on Long-Short Term Memory network that has been trained
to optimize a multi-objective function based on dice coefficient and
cross-entropy loss. The effectiveness of the proposed occlusion reconstruction
algorithm is evaluated by computing the accuracy of the popular Gait Energy
Feature on the reconstructed sequence. Experimental evaluation on public data
sets and comparative analysis with other occlusion handling methods verify the
effectiveness of our approach.
