This project focuses on developing a gait detection system leveraging computer vision techniques to recognize individuals in security footage, even when their faces and skin are obscured. Criminals often cover their faces to avoid recognition, making traditional facial recognition unreliable. By analyzing key features of an individual’s gait, such as walking style, speed, and direction, this system provides a robust alternative for visual attribution. The model tracks unique body movements and postures to create a “gait signature” that can be used to match individuals across different camera feeds or environments. This approach enhances surveillance capabilities, offering a means to attribute suspects based on behavior rather than appearance.