Gait Auth

Gait Auth is an innovative biometric authentication system that uses deep learning techniques to identify and authenticate users based on their unique walking patterns. The system analyzes acceleration data from mobile device sensors to create distinctive gait signatures for each individual.

The project employs advanced feature extraction techniques to capture the subtle characteristics of human gait, including stride length, walking rhythm, and acceleration patterns. Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM) algorithms are used to build robust classification models that can distinguish between different users with high accuracy.

The system addresses the growing need for continuous and unobtrusive authentication methods in mobile security applications. Unlike traditional biometric systems that require explicit user action, gait authentication works passively in the background, providing seamless security without disrupting user experience.

Gait Auth demonstrates the practical application of behavioral biometrics in cybersecurity, showcasing how machine learning can be used to create innovative security solutions that are both effective and user-friendly.