Robust Gait Recognition by Learning and Exploiting Sub-gait Characteristics |
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Authors: | Ibrahim Venkat Philippe De Wilde |
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Affiliation: | (1) Bourns College of Engineering, University of California, Riverside, CA 92521, USA;(2) Lawrence Berkeley National Laboratory, University of California, Cyclotron Road 1, Berkeley, CA 94720, USA |
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Abstract: | Gait recognition algorithms often perform poorly because of low resolution video sequences, subjective human motion and challenging
outdoor scenarios. Despite these challenges, gait recognition research is gaining momentum due to increasing demand and more
possibilities for deployment by the surveillance industry. Therefore every research contribution which significantly improves
this new biometric is a milestone. We propose a probabilistic sub-gait interpretation model to recognize gaits. A sub-gait
is defined by us as part of the silhouette of a moving body. Binary silhouettes of gait video sequences form the basic input
of our approach. A novel modular training scheme has been introduced in this research to efficiently learn subtle sub-gait
characteristics from the gait domain. For a given gait sequence, we get useful information from the sub-gaits by identifying
and exploiting intrinsic relationships using Bayesian networks. Finally, by incorporating efficient inference strategies,
robust decisions are made for recognizing gaits. Our results show that the proposed model tackles well the uncertainties imposed
by typical covariate factors and shows significant recognition performance. |
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