Gait as a Biometric for Person Identification in Video Sequences
| dc.contributor.author | BenAbdelkader, Chiraz | en_US |
| dc.date.accessioned | 2004-05-31T21:09:52Z | |
| dc.date.available | 2004-05-31T21:09:52Z | |
| dc.date.created | 2001-06 | en_US |
| dc.date.issued | 2001-10-10 | en_US |
| dc.description.abstract | The term gait recognition is typically used to signify the identification of individuals in image sequences `by the way they walk'. There is an increased interest in gait as a biometric, mainly due to its non-intrusive as well as non-concealable nature. Considerable research efforts are being devoted in the computer vision community to characterize and extract gait dynamics automatically from video. The objective is to use gait as a filter (or indicator) to effectively enhance the overall recognition performance of a system that uses multiple modalities. In this proposal, we describe two different gait recognition methods; a non-parametric method that uses the self-similarity plot of a walking sequence as the input feature for classification; and a parametric method that estimates the spatiotemporal parameters of gait (the cadence and stride length) and exploits their linear relationship as a cue for identification. Finally, because carried loads are gait-altering, we also present a motion-based method to detect whether a walking person carries an object (load). | en_US |
| dc.format.extent | 13408544 bytes | |
| dc.format.mimetype | application/postscript | |
| dc.identifier.uri | http://hdl.handle.net/1903/528 | |
| dc.language.iso | en_US | |
| dc.relation.isAvailableAt | Digital Repository at the University of Maryland | en_US |
| dc.relation.isAvailableAt | University of Maryland (College Park, Md.) | en_US |
| dc.relation.isAvailableAt | Tech Reports in Computer Science and Engineering | en_US |
| dc.relation.isAvailableAt | Computer Science Department Technical Reports | en_US |
| dc.relation.ispartofseries | UM Computer Science Department; CS-TR-4289 | en_US |
| dc.title | Gait as a Biometric for Person Identification in Video Sequences | en_US |
| dc.type | Technical Report | en_US |