This paper presents a spatiotemporal pyramid representation for recognizing facial expressions and hand gestures. This approach works by partitioning video sequence into increasingly fine subdivisions in the space and time domains and modeling the distribution of the local motion features inside each subdivision such that the set of motion features are mapped into spatial and temporal multi-resolution histograms. This spatiotemporal pyramid is built by weighting the histograms from the different layers of the subdivisions. The proposed approach is an extension of the orderless "bag-of-words" model by approximately capturing geometric and temporal arrangements of the local motion features. The experiments on facial expression and hand gesture data sets have demonstrated the significantly improved performance over state of art results on human activity recognition tasks by using our representation.