Kinect-based human gait identifcation under different covariate factors
DOI:
https://doi.org/10.15649/2346075X.485Keywords:
Neutral and non-neutral gait recognition; Kinect sensor; EDF; PCA; LDA; LDC.Abstract
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.
References
Bharatkumar A, Daigle K, Pandy M, Cai Q, Aggarwal J. Lower limb kinematics of human walking with the medial axis transformation. In: Proceedings of the 1994 IEEE Workshop on Motion of Non-Rigid and Articulated Objects; 1994 Nov. 11-12; Austin, TX, USA, USA; 1994. p. 70-76.
Hong S, Lee H, Nizami IF, Kim E. A new gait representation for human identification: mass vector. In: 2007 2nd IEEE Conference on Industrial Electronics and Applications. ICIEA 2007.; 2007. ICIEA 2007; 2007 May 23-25; Harbin, China; 2007. p. 669-673. https://doi.org/10.1109/ICIEA.2007.4318491
Cutting JE, Kozlowski LT. Recognizing friends by their walk: Gait perception without familiarity cues. Bulletin of the psychonomic society. 1977; 9(5): p. 353-356. https://doi.org/10.3758/BF03337021
Rida I, Almaadeed N, Almaadeed S. Robust gait recognition: a comprehensive survey. IET Biometrics. 2018;8(1):14-28. https://doi.org/10.1049/iet-bmt.2018.5063
Hsia CH, Chiang JS, Dai YJ, Lin TA. Conditional-Sorting Local Binary Pattern Based on Gait Energy Image for Human Identification. Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp). 2013 P. 1.
Hofmann M, Rigoll G. Improved gait recognition using gradient histogram energy image. In: 19th IEEE International Conference on Image Processing (ICIP); 2012 Sep 30 - Oct 3; Orlando, FL, USA. IEEE; 2012. p. 1389-1392. https://doi.org/10.1109/ICIP.2012.6467128
Han J, Bhanu B. Individual recognition using gait energy image. In.: IEEE Transactions on Pattern Analysis and Machine Intelligence; 2006;28(2):316-322. https://doi.org/10.1109/TPAMI.2006.38
BenAbdelkader C, Cutler R, Davis L. Stride and cadence as a biometric in automatic person identification and verification. In: Proceedings of Fifth IEEE International Conference on Automatic Face and Gesture Recognition; 2002 May 21-21; Washington, DC, USA, USA. IEEE; 2002. p. 372-377.
Singh JP, Jain S. Person identification based on gait using dynamic body parameters. In Trendz in Information Sciences and Computing (TISC); 2010 Dec 17-19; Chennai, India. IEEE; 2010. p. 248-252. https://doi.org/10.1109/TISC.2010.5714649
Bharti J, Gupta M. Human Gait Recognition using All Pair Shortest Path. In: 2011 International Conference on Software and Computer Applications IPCSIT.; 2011. p. 279-284.
Parajuli M, Tran D, Ma W, Sharma D. Senior health monitoring using Kinect. In: 2012 Fourth International Conference on Communications and Electronics (ICCE); 2012 Aug 1-3; Hue, Vietnam. IEEE; 2012. p. 309-312. https://doi.org/10.1109/CCE.2012.6315918
Ball A, Rye D, Ramos F, Velonaki M. Unsupervised clustering of people from'skeleton'data. In Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction; 2012: Machine Vision and Image Processing Conference, 2009. IMVIP'09. 13th International; 2012 Mar 5-8; Boston, Massachusetts, USA. IEEE; 2012. p. 225-226.
Sinha A, Chakravarty K, Bhowmick B. Person identification using skeleton information from kinect. In ACHI 2013, The Sixth International Conference on Advances in Computer-Human Interactions.; 2013. p. 101-108. https://doi.org/10.1109/SMC.2013.91
Preis J, Kessel M, Werner M, Linnhoff-Popien C. Gait recognition with kinect. In: 1st International Workshop on Kinect in Pervasive Computing.; 2012.
M. Ahmed NAJaATS. Gait recognition based on Kinect sensor. In: Real-Time Image and Video Processing ; 2014; Brussels, Belgium. https://doi.org/10.1117/12.2052588
Jiang S, Wang Y, Zhang Y, Sun J. Real time gait recognition system based on kinect skeleton feature. Springer. 2014;: p. 46--57. https://doi.org/10.1007/978-3-319-16628-5_4
Ahmed F, Paul PP, Gavrilova ML. DTW-based kernel and rank-level fusion for 3D gait recognition using Kinect. The Visual Computer, Springer; 2015 Jun 6-8; 31(6-8):915-924. https://doi.org/10.1007/s00371-015-1092-0
Sabir AT. Human gait recognition under neutral and non-neutral gait sequences. University of Buckingham, UK: PhD Thesis; 2015.
Rezaei A, Mirzakochaki S. A Novel Approach for Keyboard Dynamics Authentication based on Fusion of Stochastic Classifiers. IJCSNS. 2012; 12(8): p. 60.
Downloads
Published
How to Cite
Issue
Section
Altmetrics
Downloads
License
All articles published in this scientific journal are protected by copyright. The authors retain copyright and grant the journal the right of first publication, with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0), which permits sharing the work with authorship recognition and without commercial purposes.
Readers may copy and distribute the material from this journal issue for non-commercial purposes in any medium, provided the original work is cited and credit is given to the authors and the journal.
Any commercial use of the material from this journal is strictly prohibited without written permission from the copyright holder.
For more information on the copyright of the journal and open access policies, please visit our website.
