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Topham, LK 
ORCID: 0000-0002-6689-7944, Khan, W ORCID: 0000-0002-7511-3873, Al-Jumeily, D ORCID: 0000-0002-9170-0568, Kolivand, H ORCID: 0000-0001-5460-5679, Aldhaibani, O ORCID: 0000-0003-0235-2862 and Hussain, A
(2026)
Enabling Passive Gait Identification in Realistic and Uncontrolled Environments Using Deep Learning and Spatiotemporal Biometrics.
International Journal of Intelligent Systems (1).
ISSN 0884-8173
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Abstract
Person identification is a pivotal challenge in the security domain, with important and impactful applications such as identifying crime suspects and locating missing persons. 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-informed non-invasive gait identification (STONI-GID) 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-world environments. Moreover, the model outperformed existing methods during cross-dataset 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. Our findings will particularly advance the research frontiers of real-world gait identification and impact interdisciplinary areas of security and healthcare applications.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 0801 Artificial Intelligence and Image Processing; 1702 Cognitive Sciences; Artificial Intelligence & Image Processing; 46 Information and computing sciences |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software Q Science > QP Physiology |
| Divisions: | Computer Science and Mathematics |
| Publisher: | Wiley |
| Date of acceptance: | 12 March 2026 |
| Date of first compliant Open Access: | 20 April 2026 |
| Date Deposited: | 20 Apr 2026 13:24 |
| Last Modified: | 20 Apr 2026 13:24 |
| DOI or ID number: | 10.1155/int/9024180 |
| Editors: | Cuccureddu, F |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/28406 |
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