Person Identification using Gait by Combined Features of Width and Shape of the Binary Silhouette
Current image-based individual human recognition
methods, such as fingerprints, face, or iris biometric modalities
generally require a cooperative subject, views from certain aspects,
and physical contact or close proximity. These methods cannot
reliably recognize non-cooperating individuals at a distance in the
real world under changing environmental conditions. Gait, which
concerns recognizing individuals by the way they walk, is a relatively
new biometric without these disadvantages. The inherent gait
characteristic of an individual makes it irreplaceable and useful in
visual surveillance.
In this paper, an efficient gait recognition system for human
identification by extracting two features namely width vector of
the binary silhouette and the MPEG-7-based region-based shape
descriptors is proposed. In the proposed method, foreground objects
i.e., human and other moving objects are extracted by estimating
background information by a Gaussian Mixture Model (GMM) and
subsequently, median filtering operation is performed for removing
noises in the background subtracted image. A moving target classification
algorithm is used to separate human being (i.e., pedestrian)
from other foreground objects (viz., vehicles). Shape and boundary
information is used in the moving target classification algorithm.
Subsequently, width vector of the outer contour of binary silhouette
and the MPEG-7 Angular Radial Transform coefficients are taken as
the feature vector. Next, the Principal Component Analysis (PCA)
is applied to the selected feature vector to reduce its dimensionality.
These extracted feature vectors are used to train an Hidden Markov
Model (HMM) for identification of some individuals. The proposed
system is evaluated using some gait sequences and the experimental
results show the efficacy of the proposed algorithm.
[1] H. F. Alan J. Lipton and R. S. Patil, "Moving target classification and
tracking from real-time video," Proceedings of Fourth IEEE Workshop
on Applications of Computer Vision (WACV-98).
[2] A. N. R. N. P. C. A. K. R.-C. V. K. Amit Kale, Aravind Sundaresan and
R. Chellappa, "Identification of humans using gait," IEEE Transactions
on Image Processing, vol. 13, no. 9.
[3] A. K. R.-C. Aravind Sundaresan and R. Chellappa, "A hidden markov
model based framework for recognition of humans from gait sequences,"
Proceeding of ICIP.
[4] M. Bober, "Mpeg-7 visual shape descriptors," IEEE Transactions on
Circuit and Systems for Video Technology, vol. 11, no. 6.
[5] H. Z. Changhong Chen, Jimin Liang and H. HU, "Gait recognition using
hidden markov model," Lecture Notes on Computer Science.
[6] P. S. Huang, "Automatic gait recognition via statistical approaches for
extended template features," IEEE Transaction on Systems, Man, and
Cybernetics-Part B: Cybernetics, vol. 31, no. 5, October 2001.
[7] L. Lee and W. E. L. Grimson, "Gait analysis for recognition and
classification," Proceedings of Fifth IEEE International Conference on
Automatic Face and Gesture Recognition., 2002.
[8] W. H. Liang Wang, Tieniu Tan and H. Ning, "Automatic gait recognition
based on statistical shape analysis," IEEE Transaction on Image
processing, vol. 12, no. 9, September 2003.
[9] ÔÇöÔÇö, "Silhouette analysis-based gait recognition for human identification,"
IEEE Transaction on Image processing, vol. 25, no. 12, December
2003.
[10] D. H. Nikolaos V. Boulgouris and K. N. Plataniotis, "Gait recognition:
A challenging signal processing technology for biometric identification,"
IEEE sinal processing magazine, November 2005.
[11] M. Piccardi, "Background subtraction techniques: A review," IEEE
International Conference on Systems, Man and Cybernetics.
[12] P. W. Power and J. A. Schoonees, "Understanding background mixture
models for foreground segmentation," Proceedings of IEEE International
Conference on Image and Vision Computing.
[13] C. Stauffer and W. E. L. Grimson, "Adaptive background mixture models
for real-time video tracking," Proceedings of IEEE Computer Society
Conference on Computer Vision and Pattern Recognition, vol. 82, pp.
246-252.
[1] H. F. Alan J. Lipton and R. S. Patil, "Moving target classification and
tracking from real-time video," Proceedings of Fourth IEEE Workshop
on Applications of Computer Vision (WACV-98).
[2] A. N. R. N. P. C. A. K. R.-C. V. K. Amit Kale, Aravind Sundaresan and
R. Chellappa, "Identification of humans using gait," IEEE Transactions
on Image Processing, vol. 13, no. 9.
[3] A. K. R.-C. Aravind Sundaresan and R. Chellappa, "A hidden markov
model based framework for recognition of humans from gait sequences,"
Proceeding of ICIP.
[4] M. Bober, "Mpeg-7 visual shape descriptors," IEEE Transactions on
Circuit and Systems for Video Technology, vol. 11, no. 6.
[5] H. Z. Changhong Chen, Jimin Liang and H. HU, "Gait recognition using
hidden markov model," Lecture Notes on Computer Science.
[6] P. S. Huang, "Automatic gait recognition via statistical approaches for
extended template features," IEEE Transaction on Systems, Man, and
Cybernetics-Part B: Cybernetics, vol. 31, no. 5, October 2001.
[7] L. Lee and W. E. L. Grimson, "Gait analysis for recognition and
classification," Proceedings of Fifth IEEE International Conference on
Automatic Face and Gesture Recognition., 2002.
[8] W. H. Liang Wang, Tieniu Tan and H. Ning, "Automatic gait recognition
based on statistical shape analysis," IEEE Transaction on Image
processing, vol. 12, no. 9, September 2003.
[9] ÔÇöÔÇö, "Silhouette analysis-based gait recognition for human identification,"
IEEE Transaction on Image processing, vol. 25, no. 12, December
2003.
[10] D. H. Nikolaos V. Boulgouris and K. N. Plataniotis, "Gait recognition:
A challenging signal processing technology for biometric identification,"
IEEE sinal processing magazine, November 2005.
[11] M. Piccardi, "Background subtraction techniques: A review," IEEE
International Conference on Systems, Man and Cybernetics.
[12] P. W. Power and J. A. Schoonees, "Understanding background mixture
models for foreground segmentation," Proceedings of IEEE International
Conference on Image and Vision Computing.
[13] C. Stauffer and W. E. L. Grimson, "Adaptive background mixture models
for real-time video tracking," Proceedings of IEEE Computer Society
Conference on Computer Vision and Pattern Recognition, vol. 82, pp.
246-252.
@article{"International Journal of Electrical, Electronic and Communication Sciences:49634", author = "M.K. Bhuyan and Aragala Jagan.", title = "Person Identification using Gait by Combined Features of Width and Shape of the Binary Silhouette", abstract = "Current image-based individual human recognition
methods, such as fingerprints, face, or iris biometric modalities
generally require a cooperative subject, views from certain aspects,
and physical contact or close proximity. These methods cannot
reliably recognize non-cooperating individuals at a distance in the
real world under changing environmental conditions. Gait, which
concerns recognizing individuals by the way they walk, is a relatively
new biometric without these disadvantages. The inherent gait
characteristic of an individual makes it irreplaceable and useful in
visual surveillance.
In this paper, an efficient gait recognition system for human
identification by extracting two features namely width vector of
the binary silhouette and the MPEG-7-based region-based shape
descriptors is proposed. In the proposed method, foreground objects
i.e., human and other moving objects are extracted by estimating
background information by a Gaussian Mixture Model (GMM) and
subsequently, median filtering operation is performed for removing
noises in the background subtracted image. A moving target classification
algorithm is used to separate human being (i.e., pedestrian)
from other foreground objects (viz., vehicles). Shape and boundary
information is used in the moving target classification algorithm.
Subsequently, width vector of the outer contour of binary silhouette
and the MPEG-7 Angular Radial Transform coefficients are taken as
the feature vector. Next, the Principal Component Analysis (PCA)
is applied to the selected feature vector to reduce its dimensionality.
These extracted feature vectors are used to train an Hidden Markov
Model (HMM) for identification of some individuals. The proposed
system is evaluated using some gait sequences and the experimental
results show the efficacy of the proposed algorithm.", keywords = "Gait Recognition, Gaussian Mixture Model, PrincipalComponent Analysis, MPEG-7 Angular Radial Transform.", volume = "5", number = "8", pages = "895-8", }