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One convenient person identification method is gait identification, where individuals are identified by their unique walking style. However, traditional methods of gait identification are often affected by variations in appearance and occlusion. This work introduces a novel and robust spatiotemporal kinematics\u2010informed non\u2010invasive gait identification (STONI\u2010GID) method that uses human pose estimation, occlusion state estimation and deep machine learning. Furthermore, unlike some existing methods, we demonstrate that our method remains unaffected by everyday appearance changes, environment, or viewing angle. Our approach achieved identification accuracy of up to 98.66% when evaluated using our primary dataset of 65 diverse participants in real\u2010world environments. Moreover, the model outperformed existing methods during cross\u2010dataset validation on the large Southampton dataset and the Gait Recognition Image and Depth Dataset (GRIDDS), achieving identification accuracies of 97.68% and 99.12%, respectively. 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