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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Makihara, Y. Muramatsu, D. Iwama, H. Yagi, Y. |
| Copyright Year | 2013 |
| Description | Author affiliation: Osaka Univ., Suita, Japan (Makihara, Y.; Muramatsu, D.; Iwama, H.; Yagi, Y.) |
| Abstract | This paper describes a method of gait recognition using multiple gait features in conjunction with score-level fusion techniques. More specifically, we focus on the state-of-the-art period-based gait features such as a gait energy image, a frequency-domain feature, a gait entropy image, a chrono-gait image, and a gait flow image. In addition, we employ various types of the score-level fusion approaches including not only conventional transformation-based approaches (e.g., sum-rule and min-rule) but also classification-based approaches (e.g., support vector machine) and density-based approaches (e.g., Gaussian mixture model, kernel density estimation, linear logistic regression). In experiments, the large-population gait database with 3,249 subjects was used to measure the performance improvement in a statistically reliable way. The experimental results show 7% relative improvement on average with regard to equal error rate of the false acceptance rate and false rejection rate in verification scenarios, and also show 20% reduction of the number of candidates to be checked under 1% misdetection rate on average in screening tasks. |
| Sponsorship | IEEE Biomet. Counc. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 1306848 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781467355452 |
| e-ISBN | 9781467355469 |
| e-ISBN | 9781467355445 |
| DOI | 10.1109/FG.2013.6553797 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-04-22 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Gait recognition Feature extraction Support vector machines Hidden Markov models Computational modeling Databases Training |
| Content Type | Text |
| Resource Type | Article |
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