Gait Recognition Method Based on Wavelet Transformation and its Evaluation with Chinese Academy of Sciences (CASIA) Gait Database as a Human Gait Recognition Dataset

2012 ◽  
Author(s):  
Kohei Arai ◽  
Rosa Andrie
Keyword(s):  
Wavelet Transformation ◽  
Gait Recognition ◽  
Human Gait ◽  
Recognition Method ◽  
Chinese Academy Of Sciences ◽  
Academy Of Sciences ◽  
Gait Database

scholarly journals Skeleton based gait recognition for long and baggy clothes

2019 ◽  
Vol 277 ◽  
pp. 03005
Author(s):  
Abrar Alharbi ◽  
Fahad Alharbi ◽  
Eiji Kamioka
Keyword(s):  
Gait Recognition ◽  
User Interaction ◽  
Microsoft Kinect ◽  
Human Gait ◽  
Kinect Sensor ◽  
Recognition Method ◽  
Walking Gait ◽  
Biometric Feature ◽  
Gait Database ◽  
Microsoft Kinect Sensor

Human gait is a significant biometric feature used for the identification of people by their style of walking. Gait offers recognition from a distance at low resolution while requiring no user interaction. On the other hand, other biometrics are likely to require a certain level of interaction. In this paper, a human gait recognition method is presented to identify people who are wearing long baggy clothes like Thobe and Abaya. Microsoft Kinect sensor is used as a tool to establish a skeleton based gait database. The skeleton joint positions are obtained and used to create five different datasets. Each dataset contained different combination of joints to explore their effectiveness. An evaluation experiment was carried out with 20 walking subjects, each having 25 walking sequences in total. The results achieved good recognition rates up to 97%.

Gait Recognition-A Novel Approach to Quality Improvement in Human Silhouettes

2014 ◽  
Vol 573 ◽  
pp. 459-464 ◽  
Author(s):  
S.M.H. Sithi Shameem Fathima ◽  
R.S.D. Wahida Banu
Keyword(s):  
Quality Improvement ◽  
Recognition Performance ◽  
Gait Recognition ◽  
Recognition Algorithm ◽  
Human Gait ◽  
Blob Detection ◽  
Novel Approach ◽  
Filter Output ◽  
Chinese Academy Of Sciences ◽  
Academy Of Sciences

This paper proposes a robust algorithm for the quality improvement in human silhouettes, to improve the gait recognition percentage of a person. In silhouette based gait recognition approach, the presence of incomplete and noisy silhouettes has a direct impact on recognition performance. Using blob detection, initially the incomplete silhouettes are identified. Fusion of frame difference energy image with dominant energy image of a silhouette along with a morphological filter output, preserve the kinetic and kinematic information to make incomplete silhouette into a high quality and a complete silhouette. The results prove that the resultant silhouettes are well suited for human gait recognition algorithm with improved variance. The silhouette database is taken from CASIA database. (Institute of Automation, Chinese Academy of Sciences).

Cross-View Gait Recognition in 3-D Space Based on Gravity Center Track

2014 ◽  
Vol 644-650 ◽  
pp. 4210-4215
Author(s):  
Xin Chen ◽  
Tian Qi Yang
Keyword(s):  
Gait Recognition ◽  
Least Square Method ◽  
Least Square ◽  
Cluster Method ◽  
Direction Vector ◽  
Recognition Method ◽  
Body Area ◽  
Gravity Center ◽  
Center Track ◽  
Gait Database

We propose in this paper a novel cross-view gait recognition method based on gravity center trajectory (GCT). Inspired by the finding that if the GCT of human in walking process has regularity, the representation coefficients of the trajectory are generally consistent across different views. We propose to project the coefficients of GCT to different view plane (VP) which is the normal plane of view angle direction vector to achieve view-invariant features for gait recognition. Firstly, we obtain the GCT under different views by summation of pixel coordinates in body area. Then, we use the least square method to eliminate the upward or downward trend of GCT caused by view variance. Then, we project the GCT function to the corresponding VP. Lastly, we perform recognition by using a simple cluster method. Experimental results on the widely used CASIA-B gait database demonstrate the effectiveness and practicability of the proposed method.

scholarly journals Average Gait Differential Image Based Human Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jinyan Chen ◽  
Jiansheng Liu
Keyword(s):  
Gait Recognition ◽  
Principal Component ◽  
Memory Storage ◽  
Human Gait ◽  
Recognition Method ◽  
Feature Extraction Method ◽  
Static Information ◽  
The Difference ◽  
Gait Identification ◽  
Previous Idea

The difference between adjacent frames of human walking contains useful information for human gait identification. Based on the previous idea a silhouettes difference based human gait recognition method named as average gait differential image (AGDI) is proposed in this paper. The AGDI is generated by the accumulation of the silhouettes difference between adjacent frames. The advantage of this method lies in that as a feature image it can preserve both the kinetic and static information of walking. Comparing to gait energy image (GEI), AGDI is more fit to representation the variation of silhouettes during walking. Two-dimensional principal component analysis (2DPCA) is used to extract features from the AGDI. Experiments on CASIA dataset show that AGDI has better identification and verification performance than GEI. Comparing to PCA, 2DPCA is a more efficient and less memory storage consumption feature extraction method in gait based recognition.

scholarly journals A Robust Part based Method for Human Gait Recognition

2020 ◽  
Vol 9 (4) ◽  
pp. 1006-1009
Keyword(s):  
Support Vector Machine ◽  
Radon Transform ◽  
Gait Recognition ◽  
Lower Leg ◽  
Human Gait ◽  
Support Vector ◽  
Svm Classifier ◽  
Correct Classification ◽  
Recognition Method ◽  
Classification Procedure

This paper explored a new part based gait recognition method to address the gait covariate factors. Firstly, three robust parts such as vertical-half, head, and lower leg are cropped from the Gait Energy Image (GEI). Since, these selected parts are not affected by the major gait covariates than other parts. Then, Radon transform is applied to each selected part. Next, standard deviations are computed for the specified radial lines (i.e. angles) such as 0 0 , 300 , 600 , 900 , 1200 and 1500 , since these radial lines cover the horizontal, vertical and diagonal directions. Lastly, fuse the features of three parts at feature level. Finally, Support Vector Machine (SVM) classifier is used for the classification procedure. The considerable amount of experimental trails are conducted on standard gait datasets and also, the correct classification rates (CCR) have shown that our proposed part based representation is robust in the presence of gait covariates.

scholarly journals Preface to the Interfacial Science Developments at the Chinese Academy of Sciences Virtual Special Issue

Langmuir ◽  
2020 ◽  
Vol 36 (41) ◽  
pp. 12087-12087 ◽  
Author(s):  
Yilin Wang ◽  
Shaoyi Jiang ◽  
Zhan Chen ◽  
Shu-Hong Yu ◽  
Gilbert Walker
Keyword(s):  
Special Issue ◽  
Interfacial Science ◽  
Chinese Academy Of Sciences ◽  
Academy Of Sciences

Two new species of the spider genus Pholcus Walckenaer, 1805 (Araneae: Pholcidae) from Pakistan

Zootaxa ◽  
2020 ◽  
Vol 4820 (1) ◽  
pp. 177-185
Author(s):  
TIANQI LAN ◽  
ZHIYUAN YAO ◽  
ABID ALI ◽  
GUO ZHENG ◽  
SHUQIANG LI
Keyword(s):  
New Species ◽  
Species Group ◽  
Male And Female ◽  
Female Type ◽  
Chinese Academy Of Sciences ◽  
Two New Species ◽  
Academy Of Sciences ◽  
Gilgit Baltistan ◽  
First Time ◽  
Beijing China

The genus Pholcus Walckenaer, 1805 is reported from Pakistan for the first time. Two new species of the Pholcus nenjukovi species-group are described: Pholcus hamuchal Yao & Li sp. nov. (Gilgit Baltistan, male and female) and Pholcus kalam Yao & Li sp. nov. (Khyber Pakhtunkhwa, male and female). Type material is deposited in the Institute of Zoology, Chinese Academy of Sciences (IZCAS) in Beijing, China.

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