Anthropometric and human gait identification using skeleton data from Kinect sensor

2014 ◽  
Author(s):  
Virginia Andersson ◽  
Rafael Dutra ◽  
Ricardo Araújo
Keyword(s):  
Human Gait ◽  
Kinect Sensor ◽  
Gait Identification

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 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 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.

Gait Abnormality Detection Using Deep Convolution Network

2021 ◽  
pp. 363-372
Author(s):  
Saikat Chakraborty ◽  
Tomoya Suzuki ◽  
Abhipsha Das ◽  
Anup Nandy ◽  
Gentiane Venture
Keyword(s):  
Low Cost ◽  
Gait Pattern ◽  
Human Gait ◽  
Kinect Sensor ◽  
Gait Abnormality ◽  
Depth Images ◽  
Abnormal Gait ◽  
Unsteady Gait ◽  
Deep Convolution Network ◽  
Human Gait Analysis

Human gait analysis plays a significant role in clinical domain for diagnosis of musculoskeletal disorders. It is an extremely challenging task for detecting abnormalities (unsteady gait, stiff gait, etc.) in human walking if the prior information is unknown about the gait pattern. A low-cost Kinect sensor is used to obtain promising results on human skeletal tracking in a convenient manner. A model is created on human skeletal joint positions extracted using Kinect v2 sensor in place using Kinect-based color and depth images. Normal gait and abnormal gait are collected from different persons on treadmill. Each trial of gait is decomposed into cycles. A convolutional neural network (CNN) model was developed on this experimental data for detection of abnormality in walking pattern and compared with state-of-the-art techniques.

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%.

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