  Gait as a biometric property for person identification plays a key role in
video surveillance and security applications. In gait recognition, normally,
gait feature such as Gait Energy Image (GEI) is extracted from one full gait
cycle. However in many circumstances, such a full gait cycle might not be
available due to occlusion. Thus, the GEI is not complete giving rise to a
degrading in gait-based person identification rate. In this paper, we address
this issue by proposing a novel method to identify individuals from gait
feature when a few (or even single) frame(s) is available. To do so, we propose
a deep learning-based approach to transform incomplete GEI to the corresponding
complete GEI obtained from a full gait cycle. More precisely, this
transformation is done gradually by training several auto encoders
independently and then combining these as a uniform model. Experimental results
on two public gait datasets, namely OULP and Casia-B demonstrate the validity
of the proposed method in dealing with very incomplete gait cycles.
