We present a novel approach for human gait recognition that inherently combines appearance and motion. Dynamic texture descriptors, Local Binary Patterns from Three Orthogonal Planes (LBP-TOP), are used to describe human gait in a spatiotemporal way. We also propose a new coding of multire-solution uniform Local Binary Patterns and use it in the construction of spatiotemporal LBP histograms. We show the suitability of the representation for gait recognition and test our method on a popular CMU MoBo dataset. We then compare our result to the state of the art methods. © Springer-Verlag Berlin Heidelberg 2009.
CITATION STYLE
Kellokumpu, V., Zhao, G., Li, S. Z., & Pietikäinen, M. (2009). Dynamic texture based gait recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5558 LNCS, pp. 1000–1009). https://doi.org/10.1007/978-3-642-01793-3_101
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