Gait recognition based on video data has gained popularity in both surveillance and clinical contexts due to its unique ability to identify individuals based on walking patterns. Unlike other biometrics like fingerprints or facial recognition, gait works effectively from a distance, does not need user cooperation, handles low-quality videos, remains reliable when body traits are hidden, and is hard to imitate. Gait recognition uses either model-free or model-based methods. Model-free approaches rely on image-based features, while model-based methods focus on biomechanical features like joint kinematics. Model-based methods are easier to understand, view- and scale-invariant, and less affected by background noise, but they have greater computational complexity.