By using depth images, this paper presents an approach capable of recognizing the gesture from only one example of each class. Background removal and denoising are performed on depth images firstly. Motion Energy Information (MEI) images are then obtained through calculating the differences between consecutive frames. Within each MEI image, we represent successive movements by time series using Histograms of Oriented Gradients (HOG) descriptor. Principle Component Analysis (PCA) reconstruction approach is applied on the descriptor to find a set of discriminantly informative principle components (PCs) from the corresponding training gesture. Next the descriptors extracted from test gestures are reconstructed back utilizing each set of PCs from training gestures. Finally the test gestures are recognized according to the set of PCs which produces the lowest reconstruction error. We evaluate our approach on the task of recognizing gestures from one example using depth images, and compare the performance of our approach with other methods, reaching a promising result.