Gait Recognition Using Procrustes Shape Analysis and Shape Context

2010 ◽  
pp. 256-265 ◽  
Author(s):  
Yuanyuan Zhang ◽  
Niqing Yang ◽  
Wei Li ◽  
Xiaojuan Wu ◽  
Qiuqi Ruan
Keyword(s):  
Shape Analysis ◽  
Gait Recognition ◽  
Shape Context

The Research on Shape Context Based on Gait Sequence Image

2018 ◽  
Vol 9 (2) ◽  
pp. 21-35
Author(s):  
Rong Wang ◽  
Yongkang Liu ◽  
Mengnan Hu
Keyword(s):  
Minimum Distance ◽  
Gait Cycle ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Polar Coordinates ◽  
Target Area ◽  
Shape Context ◽  
Feature Extraction Method ◽  
Sampling Points ◽  
Gait Feature

A gait feature extraction method based on resampling shape context is proposed in this article. First, the moving target detection is carried out to obtain the target area of the human body. Second, the gait cycle is measured, and the contour points and the lower limb joints are selected as sampling points. Then, the different sampling points is placed in the polar coordinates of the origin, the number of sampling points in different cells is counted as the shape context; Finally, the feature vectors are constructed according to the shape context, and the minimum distance is used for classification and recognition. Simulation experiments based on resampling shape context are tested in CASIA gait Database A and Database B. The experimental results show that the method proposed in this article has a lower computational complexity and higher recognition rate when compared with the original shape context method, which can be used for gait recognition.

Automatic gait recognition based on statistical shape analysis

2003 ◽  
Vol 12 (9) ◽  
pp. 1120-1131 ◽  
Author(s):  
Liang Wang ◽  
Tieniu Tan ◽  
Weiming Hu ◽  
Huazhong Ning
Keyword(s):  
Shape Analysis ◽  
Gait Recognition ◽  
Statistical Shape Analysis ◽  
Statistical Shape ◽  
Automatic Gait Recognition

View-Invariant Gait Recognition Based on Head and Shoulder Shape Analysis

2014 ◽  
Vol 568-570 ◽  
pp. 705-709
Author(s):  
Guo Zhen Wang ◽  
Hua Zhang ◽  
Li Jia Wang ◽  
Zhen Jie Wang
Keyword(s):  
Gait Analysis ◽  
Shape Analysis ◽  
Gait Cycle ◽  
Average Distance ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Invariant Method ◽  
Discriminative Feature ◽  
Mean Shape ◽  
The Mean

To improve the recognition rate and resolve the view problem in gait analysis, a view-invariant method is presented. This method extracts the mean shape of the head and shoulder models in a gait cycle by using the Procrustes Shape Analysis (PSA) algorithm. Then, the mean shape is converted from 2-D space to 1-D space by unfolding the 2D mean shape from the top point of the head. Furthermore, the piecewise approximation (PA) of the unfolded mean shape is segmented corresponding to the average distance value. The obtained PA is adopted for estimating views and recognizing the gait. Finally, the presented method is conducted on the CASIA B database. The results illustrate that the PA is a powerful discriminative feature for view estimation and gait recognition, and the performance has been improved when there is view variation.

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