Gait Identification Using Limb Joint Movement and Deep Machine Learning

Person identification is a key problem in the security domain and may be used to automatically identify criminals or missing persons. The traditional face matching approaches adopted by the police and security services across the world have recently been shown to produce a high rate of false positive identification. Alternatively, gait-based person identification has shown to be a convenient method particularly as it can be performed at a distance, without the cooperation of the subject, and is a biometric trait which cannot be easily disguised. In this work, we propose a gait-based person identification approach which uses limb joint motion data and deep machine learning models to identify the individuals. Distinct statistical features are identified and extracted from limb movement using a fixed width sliding window to train a Long Short-Term Memory model. The proposed solution outperforms the existing methods producing 98.87% accuracy when evaluated over unseen samples. In addition, we propose a simple two-stage filtering approach to increase the prediction accuracy up to 100% when identifying individuals from larger sequences of samples. This finding may improve the current solutions in controlled environments such as airports. In the future, this approach may help to overcome the problem of occlusion in gait-based identification, as unlike the existing works, it does not require information regarding the entire body. The study also presents a primary dataset comprising limb joint movement acquired from a diverse range of participants during casual walking captured through two digital goniometers.