  Gait recognition refers to the identification of individuals based on
features acquired from their body movement during walking. Despite the recent
advances in gait recognition with deep learning, variations in data acquisition
and appearance, namely camera angles, subject pose, occlusions, and clothing,
are challenging factors that need to be considered for achieving accurate gait
recognition systems. In this paper, we propose a network that first learns to
extract gait convolutional energy maps (GCEM) from frame-level convolutional
features. It then adopts a bidirectional recurrent neural network to learn from
split bins of the GCEM, thus exploiting the relations between learned partial
spatiotemporal representations. We then use an attention mechanism to
selectively focus on important recurrently learned partial representations as
identity information in different scenarios may lie in different GCEM bins. Our
proposed model has been extensively tested on two large-scale CASIA-B and
OU-MVLP gait datasets using four different test protocols and has been compared
to a number of state-of-the-art and baseline solutions. Additionally, a
comprehensive experiment has been performed to study the robustness of our
model in the presence of six different synthesized occlusions. The experimental
results show the superiority of our proposed method, outperforming the
state-of-the-art, especially in scenarios where different clothing and carrying
conditions are encountered. The results also revealed that our model is more
robust against different occlusions as compared to the state-of-the-art
methods.
