Shape Variation-Based Frieze Pattern for Robust Gait Recognition

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 seq...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Seungkyu Lee, Yanxi Liu, Robert T. Collins
• Keywords: Computer Vision, CVPR 2007, Gait, Shape Cues, Svb Frieze Patterns