scholarly journals Kinect-Based Human Gait Recognition Using Locally Linear Embedded and Support Vector Machine

2018 ◽  
Vol 30 (2) ◽  
pp. 235-242
Author(s):  
Nooritawati Md Tahir ◽  
◽  
Rohilah Sahak ◽  
Ahmad Ihsan Mohd Yassin ◽  
Fadhlan Hafizhelmi Kamaruzaman
Keyword(s):  
Support Vector Machine ◽  
Gait Recognition ◽  
Human Gait ◽  
Support Vector ◽  
Locally Linear

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.

Support Vector Machine vs Pattern Network for Gait Recognition

2017 ◽  
Vol 7 (7) ◽  
pp. 352
Author(s):  
Shahla A. AbdAlKader
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Gait Recognition ◽  
Experimental Results ◽  
Human Gait ◽  
Support Vector ◽  
Identity Verification ◽  
Human Recognition ◽  
Biometric Information

Human recognition based on biometric information is important due to its reliability in identity verification. Gait recognition has ability to recognize individuals from a distance. This study includes human gait recognition based firstly on support vector machine (SVM) and secondly on PatternNet neural network. Experiments were conducted with comparisons based on the two approaches. Experimental results showed that the PattenNet neural network is more effective than the SVM in gait recognition.

scholarly journals Multimodal Few-Shot Learning for Gait Recognition

2020 ◽  
Vol 10 (21) ◽  
pp. 7619
Author(s):  
Jucheol Moon ◽  
Nhat Anh Le ◽  
Nelson Hebert Minaya ◽  
Sang-Il Choi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Gait Recognition ◽  
Gait Pattern ◽  
Recognition Problem ◽  
Human Gait ◽  
Training Dataset ◽  
Support Vector ◽  
Open Set ◽  
Latent Space

A person’s gait is a behavioral trait that is uniquely associated with each individual and can be used to recognize the person. As information about the human gait can be captured by wearable devices, a few studies have led to the proposal of methods to process gait information for identification purposes. Despite recent advances in gait recognition, an open set gait recognition problem presents challenges to current approaches. To address the open set gait recognition problem, a system should be able to deal with unseen subjects who have not included in the training dataset. In this paper, we propose a system that learns a mapping from a multimodal time series collected using insole to a latent (embedding vector) space to address the open set gait recognition problem. The distance between two embedding vectors in the latent space corresponds to the similarity between two multimodal time series. Using the characteristics of the human gait pattern, multimodal time series are sliced into unit steps. The system maps unit steps to embedding vectors using an ensemble consisting of a convolutional neural network and a recurrent neural network. To recognize each individual, the system learns a decision function using a one-class support vector machine from a few embedding vectors of the person in the latent space, then the system determines whether an unknown unit step is recognized as belonging to a known individual. Our experiments demonstrate that the proposed framework recognizes individuals with high accuracy regardless they have been registered or not. If we could have an environment in which all people would be wearing the insole, the framework would be used for user verification widely.

scholarly journals Comparison Analysis of Gait Classification For Human Motion Identification Using Embedded Computer

2018 ◽  
Vol 8 (6) ◽  
pp. 5014
Author(s):  
Agung Nugroho Jati ◽  
Astri Novianty ◽  
Nanda Septiana ◽  
Leni Widia Nasution
Keyword(s):  
Support Vector Machine ◽  
Principal Component ◽  
Human Motion ◽  
Human Gait ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Classification Methods ◽  
Comparison Analysis ◽  
Gait Classification ◽  
Embedded Computer

In this paper, it will be discussed about comparison between two kinds of classification methods in order to improve security system based of human gait. Gait is one of biometric methods which can be used to identify person. K-Nearest Neighbour has parallelly implemented with Support Vector Machine for classifying human gait in same basic system. Generally, system has been built using Histogram and Principal Component Analysis for gait detection and its feature extraction. Then, the result of the simulation showed that K-Nearest Neighbour is slower in processing and less accurate than Support Vector Machine in gait classification.

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.

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