View invariant gait recognition
In this paper, we attempt to enhance the overall recognition rate for view-invariant gait recognition. We propose a simple but efficient framework for this task with training gait sequences from multiple views. A most important problem in the framework is about the optimal choice for the training views, that is, how many views are enough to ensure a satisfying overall performance and how to combine these views to achieve the optimal performance. To solve this problem, we execute intensive experiments and give reasonable optimal choices based on the experimental results. Besides, the gait feature descriptor and the fusion method we develop for the framework also contribute to the promising results. We propose to use mean of Radon transforms of the silhouettes as the descriptor which is very competent for view-invariant application. Moreover, the combination of class correlation and view correlation is applied to score level fusion of results from different views. The CASIA database B which contains gait data from 11 views distributed uniformly in range of [0°, 180°] is chosen in our experiments.