This chapter delineates the systematic review process to analyze computational vision-based techniques for gait-based identification and gender classification. The research question focuses on understanding these techniques’ efficacy and implementation. A comprehensive search was conducted across the Scopus and Web of Science databases using a structured query, adhering to PRISMA guidelines. From an initial pool of over 3000 articles, 112 were initially considered, with 58 meeting the inclusion criteria after screening. Excluded articles were found based on thematic relationships, such as clinical gait analysis, wearable sensors, and emotion recognition. The exclusion criteria were refined to focus on gait identification with simple RGB images and exclude multi-camera setups, gender classification, and re-identification studies. Therefore, this chapter provides a detailed insight into the selection process, resulting in 56 relevant articles that form the basis of the systematic review’s subsequent analysis, aiming to reflect the current state-of-the-art in the field.