Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Nonrigid motion analysis: articulated and elastic motion
Computer Vision and Image Understanding
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Human Identification Based on Gait (The Kluwer International Series on Biometrics)
Human Identification Based on Gait (The Kluwer International Series on Biometrics)
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Bhattacharyya Distance Feature Selection
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
| Hi-index | 0.00 |
We describe an efficient and effective feature extraction and selection method for identifying humans by their gait. A sequential set of 2D stick figures is extracted from gait silhouette data by determining the joint angles and body points, and it is used to represent the gait signature that is primitive data for extracting motion parameters. The motion parameters in the gait signatures are stride length, cycle time, speed, and joint angles, and the gait features are extracted from these motion parameters. By measuring a class separability of the extracted features, important features are selected from original feature sets for classifying human in the gait patterns. Then, a k-NN classifier is used to analyze the discriminatory ability of the selected features. In experiments, higher gait classification performances, which are 96.7%, have been achieved.