In this paper, we propose a gait recognition method for extremely low frame-rate videos. Different from the popular temporal reconstruction-based methods, the proposed method uses the average gait over the whole sequence as input feature template. Assuming the effect caused by extremely low frame-rate or large gait fluctuations are intra-class variations that the gallery data fails to capture, we build a general model based on random subspace method. More specifically, a number of weak classifiers are combined to reduce the generalization errors. We evaluate our method on the OU-ISIR-D dataset with large/small gait fluctuations, and very competitive results are achieved when both the probe and gallery are extremely low frame-rate gait sequences (e.g., 1 fps).