Gait recognition, a manner to measure a person walk, has emerged as a new biometric technology due to the imperative need for efficient security infrastructure. There are several uniqueness associated with this technology: non-invasive, hard to conceal, and perceivable at a low-resolution. Unfortunately, gait exhibits large variations due to changes in view angles, clothing, footwear, carrying conditions, walking speeds, and others. Among the factors affecting gait, variation due to viewpoint change is considered as one of the greatest challenges. The appearance difference due to viewpoint change is always greater than that of identity change. Therefore, view change is regarded as one of the most challenging factors in gait recognition and a number of methods have been proposed to tackle this problem. Given the diversity of the existing approaches towards solving the view change problem in gait, this paper provides a systematic review of the literature in this subject. Categorization is made based on the nature of the methods handling view variation. Furthermore, the main features of each approach is analyzed, and the advantages and weaknesses for each of them are highlighted. Challenges and future outlook for gait recognition across view are also presented.