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 Kinect-based human gait identifcation under different covariate factors

2018 ◽  
Vol 6 (2) ◽  
pp. 1-11
Author(s):  
Azhin T. Sabir
Keyword(s):  
Human Body ◽  
Gait Recognition ◽  
Video Quality ◽  
Microsoft Kinect ◽  
Human Gait ◽  
Human Beings ◽  
Linear Discriminant ◽  
Gait Biometrics ◽  
Gait Identification ◽  
Microsoft Kinect Sensor

Introduction: Nowadays human gait identification/recognition is available in a variety of applications due to rapid advances in biometrics technology. This makes them easier to use for security and surveillance. Due to the rise in terrorist attacks during the last ten years research has focused on the biometric traits in these applications and they are now capable of recognising human beings from a distance. The main reason for my research interest in Gait biometrics is because it is unobtrusive and requires lower image/video quality compared to other biometric traits. Materials and Methods: In this paper we propose investigating Kinect-based gait recognition using non-standard gait sequences. This study examines different scenarios to highlight the challenges of non-standard gait sequences. Gait signatures are extracted from the 20 joint points of the human body using a Microsoft Kinect sensor. Results and Discussion: This feature is constructed by calculating the distances between each two joint points from the 20 joint points of the human body provided which is known as the Euclidean Distance Feature (EDF). The experiments are based on five scenarios, and a Linear Discriminant Classifier (LDC) is used to test the performance of the proposed method. Conclusions: The results of the experiments indicate that the proposed method outperforms previous work in all scenarios.

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%.

scholarly journals Gait-based Biometric Identification System using Triangulated Skeletal Models (TSM)

2021 ◽  
Vol 10 (3) ◽  
pp. 202-208
Author(s):  
Azhin Tahir Sabir
Keyword(s):  
Nearest Neighbor ◽  
Individual Recognition ◽  
Gait Recognition ◽  
User Interaction ◽  
Human Gait ◽  
Identification System ◽  
Kinect Sensor ◽  
Human Beings ◽  
Gait Identification ◽  
Leave One Out

Human gait identification is a behavioral biometric technology which can be used to monitor human beings without user interaction. Recent researches are more focused on investigating gait as one of the biometric traits.  Further, gait recognition aims to analyze and identify human behavioral activities and may be implemented in different scenarios including access control and criminal analysis. However, using various techniques in relation to image processing and obtaining better accuracy are remaining challenges. In last decade, Microsoft has introduced the Kinect sensor as an innovative sensor to provide image characteristics, precisely. Therefore, this article uses a Kinect sensor to extract gait characteristics to be used in individual recognition. A set of Triangulated shape are generated as new feature vector and called Triangulated Skeletal Model (TSM). Nearest Neighbor technique is utilized to do the recognition issue based on leave-one-out strategy. The experimental outcomes indicated that the recommended technique provides significant results and outperforms other comparative similar techniques with accuracy of 93.46%.  

A NOVEL FEATURE EXTRACTION SCHEME FOR HUMAN GAIT RECOGNITION

2010 ◽  
Vol 10 (04) ◽  
pp. 575-587 ◽  
Author(s):  
HAMED NASSAR ◽  
GHADA EL-TAWEEL ◽  
EMAN MAHMOUD
Keyword(s):  
Gait Recognition ◽  
Principal Component ◽  
Feature Space ◽  
Visual Surveillance ◽  
Human Gait ◽  
Surveillance Systems ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Extraction Scheme ◽  
Increasing Demand

With the increasing demand of visual surveillance systems, human recognition at a distance has gained extensive research interest. Gait is a potential behavioral feature to identify humans based on their motion. This paper describes a new scheme for extracting and selecting features from the gait of a human for recognition. The scheme combines both Key Fourier Descriptors (KFDs) and principal component analysis (PCA) techniques. This leads to a strength in reducing feature space by KFD, and increasing accuracy by PCA. Also, it is shown that the proposed scheme leads to a higher correct classification rate than schemes that depend on KFD alone or PCA alone.

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 Principal component analysis for human gait recognition system

2019 ◽  
Vol 8 (2) ◽  
pp. 569-576
Author(s):  
Othman O. Khalifa ◽  
Bilal Jawed ◽  
Sharif Shah Newaj Bhuiyn
Keyword(s):  
Principal Component Analysis ◽  
Minimum Distance ◽  
Gait Recognition ◽  
Principal Component ◽  
Initial Step ◽  
Component Analysis ◽  
Recognition System ◽  
Human Gait ◽  
The One ◽  
Main Components

This paper represents a method for Human Recognition system using Principal Component Analysis. Human Gait recognition works on the gait of walking subjects to identify people without them knowing or without their permission. The initial step in this kind of system is to generate silhouette frames of walking human. A number of features couldb be exytacted from these frames such as centriod ratio, heifht, width and orientation. The Principal Component Analysis (PCA) is used for the extracted features to condense the information and produces the main components that can represent the gait sequences for each waiking human. In the testing phase, the generated gait sequences are recognized by using a minimum distance classifier based on eluclidean distance matched with the one that already exist in the database used to identify walking subject.

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