We propose a novel gait recognition method that combines convolutional features with features of human pose key points obtained by a point cloud analysis model. Currently, most state-of-the-art works on gait recognition rely on only images and are purely based on convolutional neural networks. Most of these methods are very sensitive to small variations in the appearance of a walking person. For instance, if a person wears a coat or carries a bag, the accuracy of these methods may drop significantly. To address this problem, we propose to treat a sequence of human key points as a point cloud and combine human key point features and convolution feature map for final prediction. The experimental results show the promise of this approach, which outperforms three state-of-the-art baselines in all walking scenarios, including the ones involving heavy clothing or carried items.