Advancements in artificial intelligence for biometrics: A deep dive into model-based gait recognition techniques

Over the past decade, Deep Learning (DL) pipelines have undergone significant evolution and demonstrated effectiveness in addressing complex challenges within artificial intelligence domains. The construction of tailored DL pipelines for specific applications necessitates a solid grasp of deep learning principles and the range of intermediary layers at one's disposal. Crafting a DL pipeline involves leveraging appropriate datasets for the intended application and iteratively refining the pipeline by navigating through intermediary layers. The process of selecting and validating configurations demands substantial time and meticulous consideration, making it intricate to identify an optimal and resilient DL pipeline that excels across pertinent datasets. This article seeks to support researchers in comprehending diverse gait sensing technologies while establishing a foundational understanding of deep learning concepts to expedite problem-solving. A comprehensive overview of gait biometrics tailored for surveillance applications is presented herein. The fundamental aspects of deep learning pipelines are expounded upon, encompassing their selection criteria and implications for specific problems. Recent pivotal research on deep learning models is surveyed, encompassing their performance across varying application datasets. By elucidating the merits and limitations of these approaches, this work guides the derivation of an optimized pipeline achieved through a fusion of existing alternatives. The ultimate objective is to attain swifter yet precise outcomes for a given problem.