A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs

This paper studies an approach to gait based human identification via similarity learning by deep convolutional neural networks (CNNs). With a pretty small group of labeled multi-view human walking videos, we can train deep networks to recognize the most discriminative changes of gait patterns which suggest the change of human identity. To the best of our knowledge, this is the first work based on deep CNNs for gait recognition in the literature. Here, we provide an extensive empirical evaluation in terms of various scenarios, namely, cross-view and cross-walking-condition, with different preprocessing approaches and network architectures. The method is first evaluated on the challenging CASIA-B dataset in…

• NN
  National Natural Science Foundation of China

  Awards: 61525306, 61420106015
• NK
  National Key Research and Development Program of China

  Award: 2012CB316300