An important biometric method, gait recognition may identify people from a distance by their distinct gait pattern. Since the advent of deep learning, computing power has steadily increased, making automated gait analysis a breeze. Unfortunately, there are a number of factors that may impact the accuracy of identification, such as the wearer's attire, the things they are holding, the viewing angle, the presence or absence of occlusion, and malicious alterations to the datasets. Deep learning is the method of choice for this complicated issue since it can handle massive datasets. Here, we built a deep learning pipeline to process the gait dataset and a number of gait factors. Instead of using separate deep learning pipelines for each of the gait variables, as was proposed in the work, we have built a universal and thorough pipeline based on what we know about all of the gait pipelines; we have also tested our results on new datasets. By doing so, we may use a single deep learning algorithm to handle almost all of the variables. This includes dealing with gait variables based on appearance and pose-based methods. A number of strategies have been put forth in recent years that combine two types of biometrics-face, which is a physical biometric, and gait, which is a behavioural biometric-in an effort to determine if this combination outperforms methods that use just one of these. In this article, we address the pros and cons of these systems, provide an overview of some of the most well-known methods to this problem, and look at the possibilities for future studies and applications of this technology.