Context-based gait recognition
| atmire.migration.oldid | 759 | |
| dc.contributor.advisor | Gavrilova, Marina | |
| dc.contributor.author | Bazazian, Shermin | |
| dc.date.accessioned | 2013-03-06T19:51:10Z | |
| dc.date.available | 2013-06-15T07:01:49Z | |
| dc.date.issued | 2013-03-06 | |
| dc.date.submitted | 2012 | en |
| dc.description.abstract | With the increasing demand for automatic security systems capable of recognizing people from a far distance and with as less cooperation as possible, gait recognition emerged as a very popular behavioral biometric because it is remotely observable and unobtrusive. However, the complexity and the high variability of gait patterns limit the power of gait recognition algorithms and adversely affect their recognition rates. Aiming to improve the performance of gait recognition systems without sacrificing the main advantages of gait, in this thesis, I introduce a novel multimodal gait recognition system that combines the gait patterns of the subjects with the context data related to their behavioral and social patterns. To the best of my knowledge, this is one of the only examples that the social patterns of the subjects have been used as a source of information in a multimodal biometric system. This thesis introduces a well-defined framework for defining, modeling, learning, storing and matching context data in a gait recognition system. The proposed behavioral modeling and matching framework is very flexible and can easily be adapted to different applications and multimodal biometric systems. According to the conducted experiments, the proposed gait recognition system can achieve significant improvements in the performance at a very low computational cost. The comparison of the method with other existing methods in the same area shows that the proposed approach is applicable and effective. | en_US |
| dc.identifier.citation | Bazazian, S. (2013). Context-based gait recognition (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25437 | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/25437 | |
| dc.identifier.uri | http://hdl.handle.net/11023/562 | |
| dc.language.iso | eng | |
| dc.publisher.faculty | Graduate Studies | |
| dc.publisher.institution | University of Calgary | en |
| dc.publisher.place | Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | |
| dc.subject | Computer Science | |
| dc.subject.classification | Biometrics | en_US |
| dc.subject.classification | Gait recognition | en_US |
| dc.subject.classification | Information fusion | en_US |
| dc.title | Context-based gait recognition | |
| dc.type | master thesis | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | University of Calgary | |
| thesis.degree.name | Master of Science (MSc) | |
| ucalgary.item.requestcopy | true |