Name
Abstract | ||
|---|---|---|
In this paper, we systematically analyze different components of human gait, for the purpose of human identification. We investigate dynamic features such as the swing of the hands/legs, the sway of the upper body and static features like height in both frontal and side views. Both probabilistic and non-probabilistic techniques are used for matching the features. Various combination strategies may be used depending upon the gait features being combined. We discuss three simple rules: the sum, product and MIN rules that are relevant to our feature sets. Experiments using four different data sets demonstrate that fusion can be used as an effective strategy in recognition. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/ICME.2003.1221261 | Multimedia and Expo, 2003. ICME '03. Proceedings. 2003 International Conference |
Keywords | Field | DocType |
image matching,leg dynamics,features matching,multiple evidences,height,swing,combination strategies,product rules,image recognition,sum rules,dynamic features,static features,feature sets,biometrics (access control),dynamic time warping,feature extraction,human identification,MIN rules,probabilistic techniques,gait analysis,side views,gait recognition,sway,human gait,min rules,hidden Markov models,nonprobabilistic techniques,frontal views,hidden Markov model,probability | Computer vision,Data set,Pattern recognition,Gait,Computer science,Feature extraction,Gait analysis,Artificial intelligence,Probabilistic logic,Gait (human),Hidden Markov model,Swing | Conference |
Volume | ISSN | ISBN |
3 | 1520-6149 | 0-7803-7965-9 |
Citations | PageRank | References |
38 | 2.57 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Naresh P. Cuntoor | 1 | 769 | 46.67 |
| Amit Kale | 2 | 708 | 48.47 |
| Chellappa, R. | 3 | 13050 | 1440.56 |