Human Gait Recognition Based on Multi-Feature Fusion and Kinect Sensor

2019 ◽  
Author(s):  
Mingzihan Ding ◽  
Wenshu Li ◽  
Hongyan Wang ◽  
Zitong Zhao
Keyword(s):  
Feature Fusion ◽  
Gait Recognition ◽  
Human Gait ◽  
Kinect Sensor

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%.  

scholarly journals A Machine Learning Method with Threshold Based Parallel Feature Fusion and Feature Selection for Automated Gait Recognition

2020 ◽  
Vol 32 (2) ◽  
pp. 67-92 ◽  
Author(s):  
Muhammad Sharif ◽  
Muhammad Attique ◽  
Muhammad Zeeshan Tahir ◽  
Mussarat Yasmim ◽  
Tanzila Saba ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Fusion ◽  
Parallel Implementation ◽  
Gait Recognition ◽  
Color Space ◽  
Region Of Interest ◽  
Features Extraction ◽  
Human Gait ◽  
Support Vector ◽  
Data Sets

Gait is a vital biometric process for human identification in the domain of machine learning. In this article, a new method is implemented for human gait recognition based on accurate segmentation and multi-level features extraction. Four major steps are performed including: a) enhancement of motion region in frame by the implementation of linear transformation with HSI color space; b) Region of Interest (ROI) detection based on parallel implementation of optical flow and background subtraction; c) shape and geometric features extraction and parallel fusion; d) Multi-class support vector machine (MSVM) utilization for recognition. The presented approach reduces error rate and increases the CCR. Extensive experiments are done on three data sets namely CASIA-A, CASIA-B and CASIA-C which present different variations in clothing and carrying conditions. The proposed method achieved maximum recognition results of 98.6% on CASIA-A, 93.5% on CASIA-B and 97.3% on CASIA-C, respectively.

scholarly journals Human Skeleton Model Based Dynamic Features for Walking Speed Invariant Gait Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Jure Kovač ◽  
Peter Peer
Keyword(s):  
Walking Speed ◽  
Feature Fusion ◽  
Negative Impact ◽  
Recognition Performance ◽  
Gait Recognition ◽  
Real Life ◽  
Human Gait ◽  
Dynamic Features ◽  
Skeleton Model ◽  
Model Based

Humans are able to recognize small number of people they know well by the way they walk. This ability represents basic motivation for using human gait as the means for biometric identification. Such biometrics can be captured at public places from a distance without subject's collaboration, awareness, and even consent. Although current approaches give encouraging results, we are still far from effective use in real-life applications. In general, methods set various constraints to circumvent the influence of covariate factors like changes of walking speed, view, clothing, footwear, and object carrying, that have negative impact on recognition performance. In this paper we propose a skeleton model based gait recognition system focusing on modelling gait dynamics and eliminating the influence of subjects appearance on recognition. Furthermore, we tackle the problem of walking speed variation and propose space transformation and feature fusion that mitigates its influence on recognition performance. With the evaluation on OU-ISIR gait dataset, we demonstrate state of the art performance of proposed methods.

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.

scholarly journals Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7584
Author(s):  
Faizan Saleem ◽  
Muhammad Attique Khan ◽  
Majed Alhaisoni ◽  
Usman Tariq ◽  
Ammar Armghan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Deep Learning ◽  
Data Augmentation ◽  
Feature Fusion ◽  
Gait Recognition ◽  
Human Gait ◽  
Computational Time ◽  
Absolute Deviation ◽  
Whale Optimization

Human Gait Recognition (HGR) is a biometric technique that has been utilized for security purposes for the last decade. The performance of gait recognition can be influenced by various factors such as wearing clothes, carrying a bag, and the walking surfaces. Furthermore, identification from differing views is a significant difficulty in HGR. Many techniques have been introduced in the literature for HGR using conventional and deep learning techniques. However, the traditional methods are not suitable for large datasets. Therefore, a new framework is proposed for human gait recognition using deep learning and best feature selection. The proposed framework includes data augmentation, feature extraction, feature selection, feature fusion, and classification. In the augmentation step, three flip operations were used. In the feature extraction step, two pre-trained models were employed, Inception-ResNet-V2 and NASNet Mobile. Both models were fine-tuned and trained using transfer learning on the CASIA B gait dataset. The features of the selected deep models were optimized using a modified three-step whale optimization algorithm and the best features were chosen. The selected best features were fused using the modified mean absolute deviation extended serial fusion (MDeSF) approach. Then, the final classification was performed using several classification algorithms. The experimental process was conducted on the entire CASIA B dataset and achieved an average accuracy of 89.0. Comparison with existing techniques showed an improvement in accuracy, recall rate, and computational time.

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.

Video-based Indoor Human Gait Recognition Using Depth Imaging and Hidden Markov Model: A Smart System for Smart Home

2010 ◽  
Vol 20 (1) ◽  
pp. 120-128 ◽  
Author(s):  
Md. Zia Uddin ◽  
Tae-Seong Kim ◽  
Jeong Tai Kim
Keyword(s):  
Hidden Markov ◽  
Gait Recognition ◽  
Smart Homes ◽  
The Elderly ◽  
Recognition System ◽  
Home Healthcare ◽  
Human Gait ◽  
Depth Imaging ◽  
Smart System ◽  
Spatiotemporal Features

Smart homes that are capable of home healthcare and e-Health services are receiving much attention due to their potential for better care of the elderly and disabled in an indoor environment. Recently the Center for Sustainable Healthy Buildings at Kyung Hee University has developed a novel indoor human activity recognition methodology based on depth imaging of a user’s activities. This system utilizes Independent Component Analysis to extract spatiotemporal features from a series of depth silhouettes of various activities. To recognise the activities from the spatiotemporal features, trained Hidden Markov Models of the activities would be used. In this study, this technique has been extended to recognise human gaits (including normal and abnormal). Since this system could be of great significance for the caring of the elderly, to promote and preserve their health and independence, the gait recognition system would be considered a primary function of the smart system for smart homes. The indoor gait recognition system is trained to detect abnormal gait patterns and generate warnings. The system works in real-time and is aimed to be installed at smart homes. This paper provides the information for further development of the system for their application in the future.

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