Authors:
Faisel Ghazi Mohammed,Waleed khaled Eesee,DOI NO:
https://doi.org/10.26782/jmcms.2019.06.00018Keywords:
human gait recognition,gait energy image,Neural network Multi-Layer Perceptron,Abstract
The wide separation of using camera video surveillance and increasing the depending on these video to identify human identity. One of trending method to achieve this task is human gait recognition. In this paper, human gait recognized using three features include gait energy image (GEI) human body height and width. Features are easy to extract and archived high correlation to target class. Neural network Multi-Layer Perceptron used to build a recognition model to achieve 90 % accuracy.Refference:
I.Arora, P., & Srivastava, S. (2015). Gait Recognition using Gait Energy Image. 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN),4(3), 316–322.
II.Dolatabadi, E., Mansfield, A., Patterson, K. K., Taati, B., &Mihailidis, A.(2017). Mixture-Model Clustering of Pathological Gait Patterns.IEEE Journal of Biomedical and Health Informatics,21(5), 1297–1305.https://doi.org/10.1109/JBHI.2016.2633000
III.El-Alfy, H., Mitsugami, I., & Yagi, Y. (2018). Gait Recognition Based on Normal Distance Maps.IEEE Transactions on Cybernetics,48(5), 1526–1539. https://doi.org/10.1109/TCYB.2017.2705799
IV.Kumar, H. P. M., &Nagendraswamy, H. S. (2013). Gait recognition: An approach based on interval valued features.2013 International Conference on Computer Communication and Informatics, ICCCI 2013
, 0–5.https://doi.org/10.1109/ICCCI.2013.6466243
V.Li, X., & Chen, Y. (2013).Gait Recognition Based on Structural Gait Energy Image.1, 121–126.
VI.Mohualdeen, M., & Baker, M. (2018). Gait recognition based on silhouettes sequences and neural networks for human identification.Indonesian Journal of Electrical Engineering and Informatics,6(1), 110–117. https://doi.org/10.11591/ijeei.v6i1.303
VII.Nixon, M. S. (2009).Model-based gait recognition.
VIII.S. Zheng, J. Zhang, K. Huang, R. He, and T. T. (2011). Robust View Transformation Model for Gait Recognition.International Conference on Image Processing(ICIP), Brussels, Belgium.
IX.Shaikh, S. H., Saeed, K., &Chaki, N. (2014). Gait recognition using partial silhouette-based approach.
101–106.https://doi.org/10.1109/spin.2014.6776930
X.Shirke, S., Pawar, S. S., & Shah, K. (2014). Literature review: Model free human gait recognition.Proceedings – 2014 4th International Conference on Communication Systems and Network Technologies, CSNT 2014, 891–895. https://doi.org/10.1109/CSNT.2014.252
XI.Sokolova, A., &Konushin, A. (2017). Gait Recognition Based on Convolutional Neural Networks.
ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences,
XLII-2/W4(May 2017), 207–212. https://doi.org/10.5194/isprs-archives-XLII-2-W4-207-2017
XII.Tafazzoli, F., &Safabakhsh, R. (2010). Model-based human gait recognition using leg and arm movements.
Engineering Applications of Artificial Intelligence,23(8),1237–1246.https://doi.org/10.1016/j.engappai.2010.07.004
XIII.Telecomunicações, I. De, Técnico, I. S., &Lisboa, U. De. (2016). WALKING DIRECTION IDENTIFICATION USING PERCEPTUAL HASHING TanmayT .Verlekar , Paulo L . Correia.
XIV.Vinet, L., &Zhedanov, A. (2016). the Analysis for Gait Energy Image based on Statistical Methods.
Journal of Physics A: Mathematical and Theoretical,44(8), 56. https://doi.org/10.1088/1751-8113/44/8/085201