  Gait recognition has proven to be effective for long-distance human
recognition. But view variance of gait features would change human appearance
greatly and reduce its performance. Most existing gait datasets usually collect
data with a dozen different angles, or even more few. Limited view angles would
prevent learning better view invariant feature. It can further improve
robustness of gait recognition if we collect data with various angles at 1
degree interval. But it is time consuming and labor consuming to collect this
kind of dataset. In this paper, we, therefore, introduce a Dense-View GEIs Set
(DV-GEIs) to deal with the challenge of limited view angles. This set can cover
the whole view space, view angle from 0 degree to 180 degree with 1 degree
interval. In addition, Dense-View GAN (DV-GAN) is proposed to synthesize this
dense view set. DV-GAN consists of Generator, Discriminator and Monitor, where
Monitor is designed to preserve human identification and view information. The
proposed method is evaluated on the CASIA-B and OU-ISIR dataset. The
experimental results show that DV-GEIs synthesized by DV-GAN is an effective
way to learn better view invariant feature. We believe the idea of dense view
generated samples will further improve the development of gait recognition.
