Ensemble Machine Learning for Marker-Free GAIT-Based Human Authentication - B, Amogha; Deshpande, Rohini; Kumari N, Prameela (IAES Indonesian Section, 2026)
The pattern of walking is called as GAIT. This pattern can be used to authenticate persons from a distance. I-GAIT is a method where Indian people are identified and authenticated using a walking pattern. Biometric-based authentication systems are designed to authenticate people using their behavioural and physical patterns. After the COVID-19 pandemic, people are adopting to contactless authentication systems. GAIT is in great demand as authentication is contactless. This paper proposes a methodology to authenticate 240 persons who walked indoors and outdoors in a complex environment. Each person is made to walk in three different situations, such as normal walking (NW), holding a bag (HG), and wearing a coat (WC). Authentication is achieved by calculating the distance between two, four, five and cross-body human joints and morphological features. The distance of the human body is determined by extracting the landmarks from the colour image of a person using mediapipe. Features are trained using five machine learning methods such as random forest, XGboost, LightGBM, gradient boosting and logistic regression. Human recognition is performed by using hard voting, where the majority voted human class is provided as the final predicted class. Authentication accuracy to indoor database is 83%, and for the outdoor database is 86%.