  We propose a new action and gesture recognition method based on
spatio-temporal covariance descriptors and a weighted Riemannian locality
preserving projection approach that takes into account the curved space formed
by the descriptors. The weighted projection is then exploited during boosting
to create a final multiclass classification algorithm that employs the most
useful spatio-temporal regions. We also show how the descriptors can be
computed quickly through the use of integral video representations. Experiments
on the UCF sport, CK+ facial expression and Cambridge hand gesture datasets
indicate superior performance of the proposed method compared to several recent
state-of-the-art techniques. The proposed method is robust and does not require
additional processing of the videos, such as foreground detection,
interest-point detection or tracking.
