Abstract
We present a novel technique for motion-based recognition of individual gaits in monocular sequences. Recent work has suggested that the image self-similarity plot of a moving person/object is a projection of its planar dynamics. Hence we expect that these plots encode much information about gait motion patterns, and that they can serve as good discriminants between gaits of different people. We propose a method for gait recognition that uses similarity plots the same way that face images are used in eigenface-based face recognition techniques. Specifically, we first apply Principal Component Analysis (PCA) to a set of training similarity plots, mapping them to a lower dimensional space that contains less unwanted variation and offers better separability of the data. Recognition of a new gait is then done via standard pattern classification of its corresponding similarity plot within this simpler space. We use the k-nearest neighbor rule and the Euclidian distance. We test this method on a data set of 40 sequences of six different walking subjects, at 30 FPS each.We use the leave-one-out crossvalidation technique to obtain an unbiased estimate of the recognition rate of 93%.
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References
J.K. Aggarwal and Q. Cai, “Human motion analysis: a review,” in Proc. of IEEE Computer Society Workshop on Motion of Non-Rigid and Articulated Objects, 1997.
K. Akita, “Image Sequence Analysis of RealWorld Human Motion,” Vol. 17,No. 1, pp. 73–8, 1984.
C. Barclay, J. Cutting, and L. Kozlowski, “Temporal and Spatial Factors in Gait Perception that Influence Gender Recognition,” Perception and Psychophysics, Vol. 23,No. 2, pp. 145–152, 1978.
J. Bigun, G. Chollet, and G. Borgefors, Audio-and Video-based Biometric Person Authentication, Springer, 1997.
C. Bishop, Neural Networks for Pattern Recognition, Oxford: Clarendon Press, 1995.
E. Borovikov, R. Cutler, T. Horprasert, and L. Davis, “Multi-perspective Analysis of Human Actions,” 1999.
L.W. Campbell and A. Bobick, “Recognition of Human Body Motion Using Phase Space Constraints,” 1995.
C. Cedras and M. Shah, “A survey of motion analysis from moving light displays,” pp. 214–221, 1994.
D. Cunado, M. Nixon, and J. Carter, “Using Gait as a Biometric, via Phase-Weighted Magnitude Spectra,” in Proceedings of 1st Int. Conf. on Audio-and Video-Based Biometric Person Authentication, pp. 95–102, 1997.
R. Cutler and L. Davis, “Robust Real-time Periodic Motion Detection, Analysis and Applications,” Vol. 13,No. 2, pp. 129–155, 2000.
J. Cutting and L. Kozlowski, “Recognizing Friends by Their Walk: Gait Perception Without Familiarity Cues,” Bulletin Psychonomic Soc., Vol. 9,No.5, pp. 353–356, 1977.
J.W. Davis and A.F. Bobick, “The representation and recognition of action using temporal templates,” pp. 928–934, 1997.
A. Elgammal, D. Harwood, and L. Davis, “Non-parametric model for background subtraction,” in Proceedings of International Conference on Computer Vision, 2000.
D. Gavrila, “The visual analysis of human movement: a survey,” Vol. 73, pp. 82–98, January 1999.
D.M. Gavrila and L. Davis, “Towards 3-D Model-based Tracking and Recognition of Human Movement: a Multi-View Approach,” (Zurich, Switzerland), 1995.
Q. He and C. Debrunner, “Individual Recognition from Periodic Activity Using Hidden Markov Models,” in IEEE Workshop on Human Motion, 2000.
D. Hoffman and B. Flinchbaugh, “The interpretation of biological motion,” Biological Cybernetics, 1982.
D. Hogg, “Model-based vision: a program to see a walking person,” Image and Vision Computing, Vol. 1,No. 1, 1983.
T. Horprasert, D. Harwood, and L. Davis, “A Robust Background Subtraction and Shadow Detection,” 2000.
P.S. Huang, C.J. Harris, and M.S. Nixon, “Comparing Different Template Features for Recognizing People by their Gait,” in BMVC, 1998.
G. Johansson, “Visual Motion Perception,” Scientific American, pp. 75–88, June 1975.
I.T. Joliffe, Principal Component Analysis, Springer-Verlag, 1986.
J. Little and J. Boyd, “Recognizing people by their gait: the shape of motion,” Videre, Vol. 1,No. 2, 1998.
F. Liu and R. Picard, “Finding periodicity in space and time,” pp. 376–383, January 1998.
K. Luttgens and K. Wells, Kinesiology: Scientific Basis of Human Motion, Saunders College Publishing, 7th ed., 1982.
D. Meyer, J. Pösl, and H. Niemann, “Gait Classification with HMMs for Trajectories of Body Parts Extracted by Mixture Densities,” in BMVC, pp. 459–468, 1998.
H. Murase and R. Sakai, “Moving object recognition in eigenspace representation: gait analysis and lip reading,” Vol. 17, pp. 155–162, 1996.
M. Murray, “Gait as a total pattern of movement,” American Journal of Physical Medicine, Vol. 46,No. 1, pp. 290–332, 1967.
S. Niyogi and E. Adelson, “Analyzing and recognizing walking figures in XYT,” pp. 469–474, 1994.
R. Polana and R. Nelson, “Detection and Recognition of Periodic, Non-rigid Motion,” Vol. 23, pp. 261–282, June/July 1997.
B. Ripley, Pattern Recognition and Neural Networks, Cambridge: Cambridge University Press, 1996.
K. Rohr, “Towards model-based recognition of human movements in image sequences,” in CVGIP, vol. 59, 1994.
Y. Song, X. Feng, and P. Perona, “Towards Detection of Human Motion,” 2000.
P. Tsai, M. Shah, K. Keiter, and T. Kasparis, “Cyclic Motion Detection for Motion-based Recognition,” Vol. 27,No. 12, pp. 1591–1603, 1994.
M. Turk and A. Pentland, “Face Recognition using Eigenfaces,” 1991.
S. Weiss and C. Kulikowski, Computer Systems that Learn, Morgan Kaufman, 1991.
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BenAbdelkader, C., Cutler, R., Nanda, H., Davis, L. (2001). EigenGait: Motion-Based Recognition of People Using Image Self-Similarity. In: Bigun, J., Smeraldi, F. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2001. Lecture Notes in Computer Science, vol 2091. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45344-X_42
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DOI: https://doi.org/10.1007/3-540-45344-X_42
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