Person Identification from Partial Gait Cycle Using Fully Convolutional Neural Network
This addresses a practical issue in video surveillance and security applications where full gait cycles are often unavailable, though it appears incremental as it builds on existing GEI-based methods.
The paper tackles the problem of person identification from incomplete gait cycles due to occlusion by proposing a method to transform incomplete Gait Energy Images (GEI) into complete ones using a deep learning approach. Experimental results on OULP and Casia-B datasets demonstrate its validity in handling very incomplete cycles.
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.