<![CDATA[attern of walking is called as GAIT. This pattern can be used to authenticate persons from distance. I-GAIT is a method where Indian people are identified and authenticated using walking pattern. Biometric-based authentication systems are designed to authenticate people using their behavioural and physical patterns. After pandemic COVID-19, people adopting to contactless authentication systems. GAIT is in great demand as authentication is contactless. This paper proposes methodology to authenticate 240 persons who walked in indoors and outdoors in a complex environment. Each person is made to walk in three different situation such as normal walking (NW), holding bag (HG), and wearing a coat (WC). Authentication is achieved by calculating distance between two, four, five and crossbody human joints and morphological feature. Distance of human body is determined by extracting the landmarks from the color image of 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 final predicated class. Authentication accuracy to indoor database is 83% and for outdoor database is 86%.]]>
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