A Novel Human Gait Recognition Method by Segmenting and Extracting the Region Variance Feature

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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Average Gait Differential Image Based Human Recognition

The Scientific World JOURNAL ◽

10.1155/2014/262398 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 4

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.

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Gait Recognition Method Based on Wavelet Transformation and its Evaluation with Chinese Academy of Sciences (CASIA) Gait Database as a Human Gait Recognition Dataset

2012 Ninth International Conference on Information Technology - New Generations ◽

10.1109/itng.2012.164 ◽

2012 ◽

Cited By ~ 1

Author(s):

Kohei Arai ◽

Rosa Andrie

Keyword(s):

Wavelet Transformation ◽

Gait Recognition ◽

Human Gait ◽

Recognition Method ◽

Chinese Academy Of Sciences ◽

Academy Of Sciences ◽

Gait Database

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A Robust Part based Method for Human Gait Recognition

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.d7657.049420 ◽

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.

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

Indoor and Built Environment ◽

10.1177/1420326x10391140 ◽

2010 ◽

Vol 20 (1) ◽

pp. 120-128 ◽

Cited By ~ 11

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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