Human Gait recognition is one of the most promising research areas at the moment. Gait is the style or manner of walking on foot. Gait recognition aims to identify individuals by the manner in which they walk. Existing Gait representations which capture both motion and appearance information are sensitive to changes in various covariate conditions such as carrying and clothing. In this paper, we propose a novel temporal representation of Gait using Pal and Pal Entropy (GPPE) for each cycle of the silhouettes. The Principal component analysis is applied to each of the features extracted to create a feature matrix. Support Vector Machine (SVM) is used for training and testing of individuals by the proposed method. Extensive experiments on the Treadmill dataset and the CASIA datasets A, B, C have been carried out to demonstrate the effectiveness of the proposed representation of Gait.