Recently, action recognition gains a lot of attention of researchers thank to its potential applications in real life. Particularly, hand gestures, which are actions performed by human hand, have been widely studied and started to be deployed as an efficient mean of human machine interaction (HMI). In this paper, we focus on hand gestures recognition in the context of HMI which requires to balance the trade-off between recognition accuracy and computation time. While convolutional neural network (CNN) has been shown to be very effective in many tasks, it requires powerful computer and huge training data which are not always available in common use. In this paper, we study a method based on hand crafted features (i.e. dense trajectories for hand gesture representation). We then select the most significant trajectories and compute a descriptor for each of them. For final representation of a gesture, we utilize locality constrained linear coding (LLC) and compare it with Bag of Words (BoW0 model. Finally, Support Vector Machine (SVM) is deployed to classify gestures. We test the proposed method on a dataset of hand gestures captured from different viewpoints and study the impact of viewpoint changes on such dataset. Experiments show that the proposed method keeps a balance between accuracy and computational time and comparable with CNN based method.