Carnegie Mellon University
Shape Variation-based Frieze Pattern for Robust Gait Recognition

S. Lee, Yanxi Liu, and Robert Collins
Proceedings of CVPR 2007, June, 2007.

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Gait is an attractive biometric for vision-based human identification. Previous work on existing public data sets has shown that shape cues yield improved recognition rates compared to pure motion cues. However, shape cues are fragile to gross appearance variations of an individual, for example, walking while carrying a ball or a backpack. We introduce a novel, spatiotemporal Shape Variation-Based Frieze Pattern (SVB frieze pattern) representation for gait, which captures motion information over time. The SVB frieze pattern represents normalized frame difference over gait cycles. Rows/columns of the vertical/horizontal SVB frieze pattern contain motion variation information augmented by key frame information with body shape. A temporal symmetry map of gait patterns is also constructed and combined with vertical/horizontal SVB frieze patterns for measuring the dissimilarity between gait sequences. Experimental results show that our algorithm improves gait recognition performance on sequences with and without gross differences in silhouette shape. We demonstrate superior performance of this computational framework over previous algorithms using shape cues alone on both CMU MoBo and UoS HumanID gait databases.

Associated Center(s) / Consortia: Vision and Autonomous Systems Center
Associated Lab(s) / Group(s): Human Identification at a Distance and Computational Symmetry
Associated Project(s): A Computational Model for Repeated Pattern Perception using Crystallographic Groups
Number of pages: 8

Text Reference
S. Lee, Yanxi Liu, and Robert Collins, "Shape Variation-based Frieze Pattern for Robust Gait Recognition," Proceedings of CVPR 2007, June, 2007.

BibTeX Reference
   author = "S. Lee and Yanxi Liu and Robert Collins",
   title = "Shape Variation-based Frieze Pattern for Robust Gait Recognition",
   booktitle = "Proceedings of CVPR 2007",
   month = "June",
   year = "2007",