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%.  

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 Human gait identification using Kinect sensor

2017 ◽  
Vol 2 (3) ◽  
pp. 142-146 ◽  
Author(s):  
Azhin Tahir Sabir ◽  
Mohammed H. Ahmed ◽  
Abdulbasit K. Faeq ◽  
Halgurd S. Maghdid
Keyword(s):  
Gait Recognition ◽  
Temporal Variations ◽  
Human Gait ◽  
Three Dimension ◽  
Kinect Sensor ◽  
Dynamic Features ◽  
Linear Discriminant ◽  
Measured Distance ◽  
Spatio Temporal ◽  
Gait Identification

This study investigates a novel three-dimension gait recognition approach based on skeleton representation of motion by the cheap consumer level camera Kinect sensor. In this work, a new exemplification of human gait signature is proposed using the spatio-temporal variations in relative angles among various skeletal joints and changing of measured distance between limbs and land. These measurements are computed during one gait cycle. Further, we have created our own dataset based on Kinect sensor and extract two sets of dynamic features. Nearest Neighbors and Linear Discriminant Classifier (LDC) are used for classification. The results of the experiments show the proposed approach as an effective and human gait recognizer in comparison with current Kinect-based gait recognition methods.

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 Human gait recognition using preprocessing and classification techniques

2020 ◽  
Vol 10 (3) ◽  
pp. 2978
Author(s):  
Samer Kais Jameel ◽  
Jihad Anwar Qadir ◽  
Mohammed Hussein Ahmed
Keyword(s):  
Gait Recognition ◽  
Recognition Rate ◽  
Extraction Methods ◽  
Human Gait ◽  
System Efficiency ◽  
Kinect Sensor ◽  
Biometric Recognition ◽  
The People ◽  
Object Movement ◽  
Recognition Systems

Biometric recognition systems have been attracted numerous researchers since they attempt to overcome the problems and factors weakening these systems including problems of obtaining images indeed not appearing the resolution or the object completely. In this work, the object movement reliance was considered to distinguish the human through his/her gait. Some losing features probably weaken the system’s capability in recognizing the people, hence, we propose using all data recorded by the Kinect sensor with no employing the feature extraction methods based on the literature. In these studies, coordinates of 20 points are recorded for each person in various genders and ages, walking with various directions and speeds, creating 8404 constraints. Moreover, pre-processing methods are utilized to measure its influences on the system efficiency through testing on six types of classifiers. Within the proposed approach, a noteworthy recognition rate was obtained reaching 91% without examining the descriptors.

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