Human gait recognition is an emerging research topic in the biometrics research field. It has recently gained a wider interest from machine vision research community because of its rich amount of merits. In this paper, a robust energy blocks based approach is proposed. For each silhouette sequence, gait energy image (GEI) is generated. Then it is split into three blocks, namely lower legs, upper-half and head. Further, Radon transform is applied to three energy blocks separately. Then, standard deviation is used to capture the variation in radial axis angle. Finally, support vector machine classifier (SVM) is effectively used for the classification procedures. The more prominent gait covariates such as multi views, backpack, carrying, least number of frames, clothing and different walking speed conditions are effectively addressed in this work by choosing sequential, even, odd and multiple’s of three numbering frames for each sequence. Extensive experiments are conducted on four considerably large, publicly available standard datasets and the promising results are obtained.