Human Recognition System using GAIT

Abstract

GaitGate is a sophisticated human recognition system that leverages gait analysis to accurately identify individuals based on their unique walking patterns. Gait recognition offers a non-intrusive and robust biometric authentication method, capable of operating in various environments and lighting conditions. This paper presents the design and implementation ofGaitGate, highlighting its key components and functionalities. The system employs computer vision techniques to capture and analyze gait data, extracting distinctive features such as step length, stride duration, and joint angles. A machine learning algorithm is then utilized to model and classify gait patterns, enabling efficient and accurate identification of individuals. GaitGate offers real-time performance and scalability, making it suitable for applications in security systems, access control, and surveillance. Experimental results demonstrate the effectiveness and reliability of GaitGate in accurately recognizing individuals based on their gait patterns, showcasing its potential as a viable solution for human recognition in diverse scenarios.