Principal Component Analysis of Temporal and Spatial Information for Human Gait Recognition
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point-light figures
gait analysis
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Principal component analysis was applied to human gait patterns to investigate the role and relative importance of temporal versus spatial features. Datasets consisted of various limb and body angles sampled over increasingly long time intervals. We find that spatial and temporal cues may be useful for different aspects of recognition. Temporal cues contain information that can distinguish the phase of the gait cycle; spatial cues are useful for distinguishing running from walking. PCA and related techniques may be useful for identifying features used by the visual system for recognizing biological motion.
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Copyright 2004 IEEE. Reprinted from Proceedings of the 26th Annual International Conference of the IEEE EMBS, Volume 2, pages 4568-4571. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.