International Journal of Image, Graphics and Signal Processing(IJIGSP)
ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)
Published By: MECS Press
IJIGSP Vol.6, No.3, Feb. 2014
Human Identification On the basis of Gaits Using Time Efficient Feature Extraction and Temporal Median Background Subtraction
Full Text (PDF, 1178KB), PP.35-42
Gait analysis is basically referred to study of human locomotion. From the surveillance point of view behavioral biometrics and recognition at a distance are becoming more popular in researchers rather than interactive and Physiological biometrics. In this paper, a time efficient Human gait identification system is proposed. Initially Human silhouettes are extracted by using temporal median background subtraction on video frames, which successfully removes shadows and models even complex background, proposed gait algorithm extracts contours from foreground silhouettes images and then three bounding boxes are drawn around contoured human image 1) upper part for arms movement 2) middle part for thigh and knee angles 3) Lower part for legs movement, knee and ankle angles. Gait cycles are extracted to find gait period and to take final decision for gait features selection, which is used for training. Thigh, Knee, Ankle angles and bounding boxes' widths are used as gait signatures but middle portion of human contains less variations of width in gait cycle hence computing efficiency can be achieved by ignoring width factor of middle part. SVM based training and identification is performed on extracted gait features. The proposed system is assessed using publicly available gait datasets and some indoor experimental videos created for this research work. The results reveal that the proposed algorithm is able to achieve an outstanding recognition rate.
Cite This Paper
Sadaf Asif, Ali Javed, Muhammad Irfan,"Human Identification On the basis of Gaits Using Time Efficient Feature Extraction and Temporal Median Background Subtraction", IJIGSP, vol.6, no.3, pp.35-42, 2014.DOI: 10.5815/ijigsp.2014.03.05
Nikolaos V. Boulgouris, Gait Recognition Using Radon Transform and Linear Discriminant Analysis. IEEE Transactions On Image Processing, Vol. 16, No. 3, March 2007.
Sabesan Sivapalan, Daniel-Chen, Simon-Denman, SridhaSridharan and Clinton Fookes, Gait energy volumes and frontal gait recognition using depth images. International Joint Conference on Biometrics, IEEE, USA 2011.
E Adeli-Mosabbeb, M Fathya and F Zargari, Model-based human gait tracking, 3D reconstruction and recognition in uncalibrated monocular video. Imaging Science Journal vol 0, 2011.
A-Kale, A.N-Rajagopalan, N-Cuntoor and V-Kruger, Gait-based Recognition of Humans Using Continuous HMMs. IEEE Proceedings of the Fifth International Conference on Automatic Face and Gesture Recognition 2002.
C.Nandini, PratulMukhopadhyay, An Efficient Human Identification Using Gait Analysis. International Journal of Research and Reviews in Computing Engineering (IJRRCE) Vol. 1, No. 2, June 2011.
Murat Ekinci, Human Identification Using Gait. Turk J ElecEngin, VOL.14, NO.2 2006.
Atsushi Mori, Yasushi Makihara, Yasushi Yagi, Gait Recognition using Period-based Phase Synchronization for Low Frame-rate Videos. International Conference on Pattern Recognition 2010.
L-Wang, T-Tan, W-Hu, Automatic Gait Recognition Based on Statistical Shape Analysis. IEEE Transactions On Image Processing, Vol. 12, No. 9, September 2003.
Rita Cucchiara, Detecting Moving Objects, Ghosts, and Shadows in Video Streams. IEEE transactions on pattern analysis and machine intelligence, vol. 25, no. 10, October 2003.
Han-Su, Feng-Gang Huang, Human Gait Recognition Based On Motion Analysis. Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 18-21 August 2005.
J-hutler,M. Nixon, C. Harris, Statistical gait recognition via temporal moments. In Proc. 4th IEEE Southwest Symp. Image Analysis and Interpretation, 2000, pp. 291–295.
J. B-Hayfron-Acquah, M. S-ixon, and J. N. Carter, Automatic gait recognition by symmetry analysis. In Proc. 3rd Int. Conf. Audio- and Video-Based Biometric Person Authentication, 2001.
J. Foster, M. Nixon, A. Prugel-Bennett, New area based metrics for gait recognition. In Proc. 3rd Int. Conf. Audio- and Video-Based Biometric Person Authentication, 2001.
Baofeng Guo,Mark S. Nixon, Gait Feature Subset Selection by Mutual Information. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems And Humans, Vol. 39, No. 1, January 2009.
Chen Wang, Junping Zhang, Liang Wang, Human Identification Using Temporal Information Preserving Gait Template. In IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, No. 11, November 2012.
Guoying Zhao,Li Cui,Hua Li,Matti Pietikainen, Gait Recognition Using Fractal Scale and Wavelet Moments.Iin IEEE ICPR 2006.
Tan, Tienjui N.; Hu, Weinming; Ning, Huazhong, Automatic Gait Recognition based on Statistical Shape Analysis, IEEE Transactions on Image Processing, Vol 12, Issue 9.