A multi-view gait recognition method using recovered static body parameters of subjects is presented; we refer to these parameters as activity-specific biometrics. Our data consists of 18 subjects walking at both an angled and frontal-parallel view with respect to the camera. When only considering data from a single view, subjects are easily discriminated; however, discrimination decreases when data across views are considered. To compare between views, we use ground truth motion-capture data of a reference subject to find scale factors that can transform data from different viewsi nto a common frame ("walking-space"). Instead of reporting percent correct from a limited database, we report our results using an expected confusion metric that allows us to predict how our static body parameters filter identity in a large population: lower confusion yields higher expected discrimination power. We show that using motion-capture data to adjust vision data of different views to a common reference frame, we can get achieve expected confusions rateson the order of 6%. © Springer-Verlag 2001.
CITATION STYLE
Johnson, A. Y., & Bobick, A. F. (2001). A multi-view method for gait recognition using static body parameters. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2091 LNCS, pp. 301–311). Springer Verlag. https://doi.org/10.1007/3-540-45344-x_44
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