  Gait-based person identification from videos captured at surveillance sites
using Computer Vision-based techniques is quite challenging since these walking
sequences are usually corrupted with occlusion, and a complete cycle of gait is
not always available. In this work, we propose an effective neural
network-based model to reconstruct the occluded frames in an input sequence
before carrying out gait recognition. Specifically, we employ LSTM networks to
predict an embedding for each occluded frame both from the forward and the
backward directions, and next fuse the predictions from the two LSTMs by
employing a network of residual blocks and convolutional layers. While the
LSTMs are trained to minimize the mean-squared loss, the fusion network is
trained to optimize the pixel-wise cross-entropy loss between the ground-truth
and the reconstructed samples. Evaluation of our approach has been done using
synthetically occluded sequences generated from the OU-ISIR LP and CASIA-B data
and real-occluded sequences present in the TUM-IITKGP data. The effectiveness
of the proposed reconstruction model has been verified through the Dice score
and gait-based recognition accuracy using some popular gait recognition
methods. Comparative study with existing occlusion handling methods in gait
recognition highlights the superiority of our proposed occlusion reconstruction
approach over the others.
