  Gait as a biometric trait has attracted much attention in many security and
privacy applications such as identity recognition and authentication, during
the last few decades. Because of its nature as a long-distance biometric trait,
gait can be easily collected and used to identify individuals non-intrusively
through CCTV cameras. However, it is very difficult to develop robust automated
gait recognition systems, since gait may be affected by many covariate factors
such as clothing, walking speed, camera view angle etc. Out of them, large view
angle changes has been deemed as the most challenging factor as it can alter
the overall gait appearance substantially.
  Existing works on gait recognition are far from enough to provide satisfying
performances because of such view changes. Furthermore, very few works have
considered evidences -- the demonstrable information revealing the
reliabilities of decisions, which are regarded as important demands in machine
learning-based recognition/authentication applications. To address these
issues, in this paper we propose a Discriminant Gait Generative Adversarial
Network, namely DiGGAN, which can effectively extract view-invariant features
for cross-view gait recognition; and more importantly, to transfer gait images
to different views -- serving as evidences and showing how the decisions have
been made. Quantitative experiments have been conducted on the two most popular
cross-view gait datasets, the OU-MVLP and CASIA-B, where the proposed DiGGAN
has outperformed state-of-the-art methods. Qualitative analysis has also been
provided and demonstrates the proposed DiGGAN's capability in providing
evidences.
