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Efficient Gait Recognition of Individuals by Utilizing Shape Features Using Particle Swarm Optimization
Abstract
Gait is the manner in which a person walks and has gained much importance in the recent past in surveillance systems.. This method uses the concept of extracting the features from the video sequence. which are used to identify the individual. In this work, along with the most effective parts and more informative less effective parts, which are extracted due to the effect of various cofactors, shape features are also considered for recognition. This work utilizes only the most informative movable parts with fixed movement as the shape of the parts tends to change with motion. The performance of this method is compared against gait recognition using PSO without utilizing the shape features. The experimental results shows that the proposed method of PSO utilizing shape features shows better performance metrics when compared to the recognition using PSO without considering the shape features.
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References
A. Jain, R. Bolle, and S. Pankanti, Biometrics: Personal Identification in Networked Society. Kluwer Academic Publishers, 1999.
Gafurov D. A Survey of Biometric Gait Recognition: Approaches, Security and Challenges. NIK’2007 Conferences; 2007.
Liang Wang, Tieniu Tan,HuazhongNing, and Weiming Hu,”Silhouette Analysis-Based Gait Recognition for Human Identification”,IEEE transactions on pattern analysis and machine intelligence, vol. 25, no. 12, December 2003.
Iwashita, Yumi, Koji Uchino and Ryo Kurazume.; "Gait-Based Person Identification Robust to Changes in Appearance", Sensors 13, No. 6, pp. 7884-7901, 2013.
Z. Zhou, A.P. Bennett, R. Damper, A Bayesian framework for extracting human gait using strong prior knowledge, IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (11) (2006) 1738–1752.
J. Han, B. Bhanu, Individual recognition using gait energy image, Transactions on Pattern Analysis and Machine Intelligence 28 (2) (2006) 316–322.
L. Lee, W. Grimson, Gait analysis for recognition and classification, in: Proceedings of the Fifth IEEE Conference on Face and Gesture Recognition, vol. 1, 2002, pp. 155–161.
S. Lee, Y. Liu, R. Collins, Shape variation-based frieze pattern for robust gait recognition, in: Proceedings of the 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Minneapolis, USA, 2007, pp. 1–8.
M. A. Hossain , Y. Makihara , W. Junqui and Y. Yagi , "Clothing-invariant gait identification using part-based clothing categorization and adaptive weight control" , Pattern Recognition , vol. 43 , no. 6 , pp.2281 -2291 , 2010.
C. BenAbdelkader, R. Culter, H. Nanda, and L. Davis, “EigenGait:Motion-Based Recognition of People Using Image Self-Similarity,”Proc. Int’l Conf. Audio- and Video-Based Biometric Person Authentication,pp. 284-294, 2001
H. Murase and R. Sakai, “Moving Object Recognition in Eigenspace Representation: Gait Analysis and Lip Reading,” Pattern Recognition Letters, vol. 17, pp. 155-162, 1996.
P. Huang, C. Harris, and M. Nixon, “Human Gait Recognition in Canonical Space Using Temporal Templates,” IEE Proc. Vision Image and Signal Processing Conf., vol. 146, no. 2, pp. 93-100, 1999.
S. Niyogi and E. Adelson, “Analyzing and Recognizing Walking Figures in XYT,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, pp. 469-474, 1994.
C. BenAbdelkader, R. Cutler, and L. Davis, “Motion-Based Recognition of People in EigenGait Space,” Proc. Int’l Conf.Automatic Face and Gesture Recognition, pp. 267-274, 2002.
M.Aasha and S.Sivakumari, “An Efficient Gait based Recognition using Bat Algorithm,”International Journal of Computer Applications, vol 132, no 9, pp. 0975 – 8887, December 2015.
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