Name
Abstract | ||
|---|---|---|
Gait is a spatio-temporal phenomenon that typifies the motion characteristics of an individual. In this paper, we propose a view based approach to recognize humans through gait. The width of the outer contour of the binarized silhouette of a walking person is chosen as the image feature. A set of stances or key frames that occur during the walk cycle of an individual is chosen. Euclidean distances of a given image from this stance set are computed and a lower dimensional observation vector is generated. A continuous HMM is trained using several such lower dimensional vector sequences extracted from the video. This methodology serves to compactly capture structural and transitional features that are unique to an individual. The statistical nature of the HMM renders overall robustness to gait representation and recognition. Human identification performance of the proposed scheme is found to be quite good when tested in natural walk conditions. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/AFGR.2002.1004176 | FGR |
Keywords | Field | DocType |
biometrics (access control),computer vision,gait analysis,hidden Markov models,image motion analysis,vectors,video signal processing,Euclidean distances,automated person identification,binarized silhouette outer contour width,continuous hidden Markov model,gait representation robustness,gait-based human recognition,human identification performance,image feature,individual motion characteristics,key frames,lower-dimensional observation vector sequences,natural walking conditions,spatio-temporal phenomenon,stance set,structural features,transitional features,video,view-based approach,walk cycle,walking person | Computer vision,Machine vision,Pattern recognition,Gait,Silhouette,Computer science,Robustness (computer science),Gait analysis,Artificial intelligence,Access control,Euclidean geometry,Hidden Markov model | Conference |
ISBN | Citations | PageRank |
0-7695-1602-5 | 90 | 8.69 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amit Kale | 1 | 708 | 48.47 |
| A. N. Rajagopalan | 2 | 1106 | 92.02 |
| Naresh P. Cuntoor | 3 | 769 | 46.67 |
| Kruger, V. | 4 | 90 | 8.69 |