Considering the problem of gait based gender recognition when gait information can be acquired from multiple
views, this paper presents a detailed analysis on how to combine different views and proposes a fusion method derived
from Bayesian theory. For feature extraction, a spatio-temporal gait representation is adopted and improved to reduce data
redundancies. Then the class separability of each view angle is analyzed by using such features and the gender recognition
rate is also computed under every single view. Next, three kinds of fusion scheme are designed to combine these different
view angles for a comparison. Experiments are implemented on CASIA Gait Database (Dataset B) and the results demonstrate
that the proposed fusion method achieves the superior recognition performance of 97.5% in large datasets.