With advancements in biometric securities, focus has increased on utilizing gait as a means of recognition. Gait describes the unique walking pattern present in humans and has shown promising results in person re-identification tasks. Unlike other biometric features, gait is unique in that it is a subconscious behavior minimizing the risk of purposeful obfuscation. In this research, we first cover supervised approaches showing that current methods fail to learn a unique signature that describes the motion of a subject. Rather they extract frame-based feature information which is then aggregated. While these methods have shown to be effective, they do not solve the underlying problem of trying to extract a gait signature. In this research, a novel approach is proposed that utilizes motion information to extract a true gait signature. Utilizing the repetitive nature of body part motion, we argue that since each subject has a unique gait, they will also have a unique repetition pattern. From a walking sequence, we are able to extract a frame-based similarity matrix that effectively shows a unique repetitive pattern. We further explore this idea using unsupervised learning and show that this unique repetition pattern performs well in both multi-view and cross-covariate scenarios. Currently there are no unsupervised methods for gait recognition, so this foundational work acts as a guideline for future research and evaluation. In addition, the proposed unsupervised method outperforms many supervised methods.
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Rawat, Yogesh Singh
Master of Science (M.S.)
College of Engineering and Computer Science
Length of Campus-only Access
Masters Thesis (Open Access)
Matasa, Alexander, "A Deep Learning Approach for Learning Human Gait Signature" (2021). Electronic Theses and Dissertations, 2020-. 527.