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.

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).

Model-Based Human Gait Recognition Via Deterministic Learning

2013 ◽  
Vol 6 (2) ◽  
pp. 218-229 ◽  
Author(s):  
Wei Zeng ◽  
Cong Wang ◽  
Yuanqing Li
Keyword(s):  
Gait Recognition ◽  
Human Gait ◽  
Deterministic Learning ◽  
Model Based

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 Cross-View Gait Recognition Based on Dual-Stream Network

2021 ◽  
Vol 25 (5) ◽  
pp. 671-678
Author(s):  
Xiaoyan Zhao ◽  
Wenjing Zhang ◽  
Tianyao Zhang ◽  
Zhaohui Zhang ◽  
◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Recognition Performance ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Lower Limbs ◽  
Viewing Angle ◽  
Dynamic Features ◽  
Stream Network ◽  
Long Distance ◽  
Academy Of Sciences

Gait recognition is a biometric identification method that can be realized under long-distance and no-contact conditions. Its applications in criminal investigations and security inspections are thus broad. Most existing gait recognition methods adopted the gait energy image (GEI) for feature extraction. However, the GEI method ignores the dynamic information of gait, which causes the recognition performance to be greatly affected by viewing angle changes and the subject’s belongings and clothes. To solve these problems, in this paper a cross-view gait recognition method that uses a dual-stream network based on the fusion of dynamic and static features (FDSN) is proposed. First, the static features are extracted from the GEI and the dynamic features are extracted from the image sequence of the human’s lower limbs. Then, the two features are fused, and finally, a nearest neighbor classifier is used for classification. Comparative experiments on the CASIA-B dataset created by the Automation Institute of the Chinese Academy of Sciences showed that the FDSN achieves a higher recognition rate than a convolutional neural network (CNN) and Gaitset under changes in viewing angle or clothing. To meet our requirements, in this study a gait image dataset was collected and produced in a campus setting. The experimental results on this dataset show the effectiveness of the FDSN in terms of eliminating the effects of disruptive changes.

scholarly journals Gait Recognition for Human-Exoskeleton System in Locomotion Based on Ensemble Empirical Mode Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jing Qiu ◽  
Huxian Liu
Keyword(s):  
Empirical Mode Decomposition ◽  
Recognition Performance ◽  
Human Subjects ◽  
Gait Recognition ◽  
Ensemble Empirical Mode Decomposition ◽  
Machine Learning Algorithms ◽  
Human Gait ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Phase Recognition

As exoskeleton robots are more frequently applied to impaired people to regain mobility, detection and recognition of human gait motions is important to prepare suitable control modes for exoskeletons. This paper proposes to explore the potential of the ensemble empirical mode decomposition (EEMD) method to help analyze and recognize gait motions for human subjects who wear the exoskeleton to walk. The intrinsic mode functions (IMFs) extracted from the original gait signals by EEMD are utilized to act as inputs for classification algorithms. Evident correlations are found between some IMFs and original gait kinematic sequences. Experimental results on gait phase recognition performance on 14 able-bodied subjects are shown. The performance of the composing signals extracted from the original signals as IMF 1 ∼ IMF 8 is investigated, which indicates that IMF 8 might be helpful when wearing exoskeleton and IMF 5 might be helpful when walking without exoskeleton on gait recognition. And the similarity of joint synergy between wearing and without wearing exoskeleton is analyzed, and the result shows that the joint synergy might change between with and without wearing exoskeleton. The quantitative results show that based on some IMFs of the same orders, these machine learning algorithms can achieve promising performances.

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.

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