This paper presents human action recognition by using shape and texture based DMM-Haar features where collaborative representation classifier is adopted for action classification. In this study, we have introduced effective feature extraction technique based on Depth Motion Maps (DMMs) and Haar wavelet transformation, where different actions can be represented with a range of features. Firstly, we have calculated three DMMs such as DMM front view, top view and side view from 3D action video sequences as the shape features. After that, we have utilized Haar wavelet on the DMMs generated images to extract texture information and concatenated all features as a feature matrix. We have utilized principal component analysis for reducing the feature dimensions of the feature matrix. Finally, l2 normed based collaborative representative classification technique is adopted to classify different actions. For this research, we have analyzed the effects of the DMM-Haar features on experimental basis with DMM features based results. The performance study of the proposed method is comparable with the state-of-the-art methods to recognize human action on the publicly available Microsoft Research Action 3D dataset.