person identification using gait: svm classifier approach
DESCRIPTION
Person identification from a distance has got lot of importance in the field of security and visual surveillance. Gait biometrics play vital role to recognize a person via style of his walking. On the basis of their walking, proposed method used to identify the person during their walking. This method is evaluated on CASIA gait database by using support vector machine classifier on the basis of kernel functions. Experiment results shows that the classification ability of SVM classifier using quadratic kernel is perform better than redial basis, polynomial, linear type kernel function. This method is more efficient on the basis of their recognition rate and their computational complexity.TRANSCRIPT
International Journal of Emerging Technologies and Engineering (IJETE) Volume 1 Issue 10, November 2014, ISSN 2348 – 8050
261
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Person Identification Using Gait: SVM Classifier Approach
P.B.Shelke*, P.R.Deshmukh** *Department of Electronics, PLITMS, Buldana, India
**Department of Electronics, Sipna College of Engg&Technology, Amravati, India
ABSTRACT
Person identification from a distance has got lot of
importance in the field of security and visual surveillance.
Gait biometrics play vital role to recognize a person via
style of his walking. On the basis of their walking,
proposed method used to identify the person during their
walking. This method is evaluated on CASIA gait
database by using support vector machine classifier on the
basis of kernel functions. Experiment results shows that
the classification ability of SVM classifier using
quadratic kernel is perform better than redial basis,
polynomial, linear type kernel function. This method is
more efficient on the basis of their recognition rate and
their computational complexity.
Keywords- CASIA, DWT method, Gait biometrics, SVM
classifier, Visual Surveillance.
I. INTRODUCTION Gait biometrics play very important role in person
identification at a distance. The fusion of human gait and
biometrics [1] has become a popular research direction
over the past few years. This interest is strongly driven by
the need for automated person identification systems for
visual surveillance and monitoring applications in
security-sensitive environments such as malls, parking
lots, and airports. It aims to discriminate individuals by the
way they walk. In comparison with other biometrics gait
has some unique features such as it is unobtrusive in
natures. It can be captured at a distance without prior
consent of the observed object. Gait also has advantages of
being difficult to hide and steal [10]. This paper is divided
into five sections: Section I Introduction.Section II
Literature review Section III Proposed Method Sections
IV.Experiment and Results finally Conclusion is presented
in section V.
II. LITERATURE REVIEW Generally gait identification approaches are categorized
into two classes namely model based methods and model
free based methods[10].In the model-based methods, the
human body silhouette is model and then the its features
are extracted by the measure of structural components of
models or by the motion trajectories of body parts
[2,3,4,5]. Most existing model free approaches can be
further divided into two main classes, state-space methods
and spatiotemporal methods [6, 7, 8, 9]. In the state-space
methods consider gait motion to be composed of a
sequence of static body poses, and recognize it by
considering temporal variations observations with respect
to those static pose. The spatiotemporal method
characterizes the spatiotemporal distribution generated
during their gait motion. Model based approach it has high
computational complexity and more difficult in low
resolution images so found difficulty in real time system
due to feature extraction process. In model free approach
as its computational complexity remain low, this approach
is well suitable for real time system as it is easy to extract
the features comparatively.
III. PROPOSED METHOD
Fig.1 Proposed Method
As shown in fig.1the proposed method consist of four
steps namely, preprocessing, segmentation, feature
extraction, classification which are described as fallows.
3.1 Preprocessing
International Journal of Emerging Technologies and Engineering (IJETE) Volume 1 Issue 10, November 2014, ISSN 2348 – 8050
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Once video of the walking object is captured using static
camera its binary silhouette is extracted By using
approximate median background subtraction method. In
this method frame difference between current frame and
known background frame is computed and is compared to
predetermined threshold level. If this difference is greater
than threshold then it is foreground frame otherwise it
considered as background frame. For this method it is
assumed that camera is remaining static. By using erosion
and dilation technique, irregularities present in extracted
silhouettes are removed.
3.2 Segmentation
While walking the person, variation take place
corresponds to different parts of the human body. Binary
silhouette is segmented into six region components to
extract gait features to improve the accuracy .
3.3 Feature Extraction
Discrete wavelet transform (DWT) is one of the useful
tool to obtain the additional information from raw image
by using decomposition technique. In discrete wavelet
transform image become decomposed into low frequency
course approximation information and high frequency
detail information by using low pass and high pass filters .
In single level decomposition image result in four sub-
frequency bands such as approximation, horizontal,
vertical, diagonal sub bands accordingly to their
decomposition levels. Notice that the detail coefficients
are small and consist mainly of high-frequency
information, while the approximation coefficients contain
only the low frequency information.
For this Experiment, we have used single level-two
dimensional discrete wavelet transform operated on six
different components of human body silhouette .In this
method we have used coiflet4 wavelet kernel which is
operated on approximation subband. Coiflet4 is
orthogonal wavelet family kernel of N= 4th order.
3.4 Classification
SVM is a relatively new learning machine technique,
which is based on the principle of structural risk
minimization. It is basically two class classifier that
optimally separates the two classes of data with maximum
margin. To construct an optimal hyper plane, SVM
employs an iterative training algorithm, which is used to
minimize an error function.
Consider a set of training (x1,y1),…,(xi,yi) where input xi є
RN
and class labels yi є {-1,+1}
. . .(1)
where, yi represents the class to which input belongs.
Here, SVM will find a separating plane (hyperplane) that
divides the data into two classes represented by yi= 1 and
yi= -1, with maximum margin. Support vector machine
(SVM) [15,16] require the solution of the following
optimization problem to the minimization of the error
function:
. . . . . . (2)
Subject to the constraints:
.
. . . . . . (3)
where C is the capacity constant, w is the vector of
coefficients, b is a constant, and represent Lagrange
multiplier parameter for handling non separable data
(inputs). The index i labels the N training cases. Note that
represents the class labels and xi represents the
independent variables(features). The kernel is used to
transform data from the input (independent) to the feature
space. It should be noted that the larger the C, the more the
error is penalized. Thus, C should be chosen with care to
avoid over fitting.
IV. EXPERIMENT & RESULTS The proposed method is tested on both CASIA A and
CASIA B gait database by considering its side view . For
this experiment we have used ten different objects with
side view video consideration. Each object consists of
35frames with 2.5 gait cycle. for this we used tenfold
cross validation method for reliable accuracy.
For this experiment we have considered linear, radial
basic function, polynomial and quadratic kernels function
along with three methods using Quadratic(QP),sequential
minimal optimization(SMO)and least square(LS)
techniques. In this experiment default value of their
parameters has been used. Table1and Table 2, shows the
results of correct classification rate(CCR) and speed
comparison using linear, radial basic function, polynomial
and quadratic kernels function. Fig.2 shows correct
classification rate (CCR) comparison using this kernel
function. From this we have get 92.08% recognition
accuracy using linear kernel and least square technique
similarly we get 98.70% recognition rate using quadratic
kernel and QP technique.
As we know that computational complexity is most
important parameter to check the speed of the any system.
International Journal of Emerging Technologies and Engineering (IJETE) Volume 1 Issue 10, November 2014, ISSN 2348 – 8050
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As the elapsed time called as central processing time
decreases, its computational complexity decreases hence it
speed increases and viceversa.Fig.3 shows the speed
comparison using kernel functions to check its
computational complexity. From this we get two second
elapsed time for quadratic with LS technique and also we
found 328.29 second elapse times for quadratic kernel
with QP technique. With the tradeoff between correct
classification rate and speed, we get 97.37% classification
rate for 2second elapsed time. From this we conclude that
quadratic kernel using least square method is most
efficient and has lower computational complexity.
We compared the proposed method with other well cited
gait recognition approaches using CASIA (formerly
NLPR) gait database which result are given in Table 3 and
fig.4. From this, it is clearly seen that the proposed method
perform better than existing algorithms.
Table1: Correct classification rate comparisons using
different kernels function
KERNELS QP SMO LS
Linear 95.2 94.45 92.08
Rbf 96.87 97.51 96.87
Polynomial 98.54 98.25 97.28
Quadratic 98.79 98.28 97.37
Table 2: Elapsed time (in second) comparisons using
different kernels function
KERNELS QP SMO LS
Linear 225.76 207.15 3.01
Rbf 45.75 5.72 2.30
Polynomial 336.61 7.10 2.00
Quadratic 328.89 11.78 2.00
Table 3: Comparison of proposed algorithm with existing
algorithms
Algorithms % Correct
classification rate
Collins (2002) [2] 71.25
Wang (2003)[10] 88.75
Lu (2006) [11] 82.50
Bo Ye (2007)[12] 88.75
Sungjun Hong (2009)[13] 90.00
L.Sudha (2011)[14] 97.91
Proposed method 98.25
Fig.2 Result of recognition rate comparison.
Fig.3 Speed analysis for different kernels function.
Fig.4 Performance result of proposed algorithm with
existing algorithms.
V. CONCLUSION The proposed method is tested on CASIA gait database by
using SVM classifier. Feature extraction is take place
using coiflet4 wavelet method with tenfold cross
validation method. Experimental result shows that 92.08%
recognition accuracy for linear kernel and least square
technique and 98.70% recognition rate using quadratic
kernel with QP technique.
From this we conclude that quadratic kernel using least
square method is most efficient and fast method than
International Journal of Emerging Technologies and Engineering (IJETE) Volume 1 Issue 10, November 2014, ISSN 2348 – 8050
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redial basis, polynomial,linear kernel methods used in this
algorithm. This algorithm can be employed in automated
person identification Systems .However to increase the
efficiency of this algorithm to be suited for all forms of
person identification; some future work has to be done by
modifying classification method.
REFERENCES [1]. A.Jain, R.Bolle and S.Pankanti, Bimetrics: Personal
Identification in NetworkedSociety, KluwerAcademic
pulisher, 1997.
[2]. R. Collins, R. Gross, and J. Shi,“Silhouette-Based
Human Identification from Body Shape and Gait”, Proc.
Int’l Conf. Automatic Face and Gesture Recognition,
pp.366-371,2002.
[3]. N. V. Boulgouris, D. Hatzinakos, and K. N.
Plataniotis,“Gait recognition: A challenging signal
processing technology for biometric identification”, IEEE
Signal Processing Magazine, Vol.22, No. 6, pp. 78-
90,2005.
[4]. N. Huazhong, T. Tieniu, W. Liang, and H.Weiming,
“Kinematics-based tracking of human walking in
monocular video sequences”, Image and Vision
Computing, pp. 429- 441, 2004.
[5]. C. Y. Yam, M. S. Nixon, and J. N. Carter, “Gait
recognition by walking and running: A model based
Approach”, Proceedings of 5th Asian Conference on
Computer Vision, pp. 1-6, 2002.
[6]. 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
[7]. J. Han and B. Bhanu, “Individual recognition using
Gait Energy Image”, IEEE Transactions on Pattern
Analysis and Machine Intelligence, Vol. 28, No. 2, pp.
316- 322.2006.
[8]. Y.Chai, Q. Wang, R. Zhao, and C. Wu, “New
automatic gait recognition method based on the perceptual
curve”, Proceedings of IEEE TENCON 2005, pp.1-5,
2005.
[9]. C. P. Shi, H. G. Li, X. Lian, and X. G. Li.,“Multi-
resolution local moment feature for gait recognition”,
Proceedings of the 5th International Conference on
Machine Learning and Cybernetics, 3709-3714, 2006.
[10].L.Wang,T.Tan,H.Ning,and W. Hu “Silhouette
Analysis-Based Gait recognition for Human
Identification”, IEEE Trans. on Pattern Analysis and
Machine Intelligence, Vol. 25, pp.1505-1518,2003.
[11] J. Lu, E. Zhang, and C. Jing, (2006) “Gait recognition
using wavelet descriptors and independent component
analysis, Proceedings of the 3rd International Symposium
on Neural Networks, pp. 232- 237,2006.
[12].Bo Ye,Yu-mei wen“Gait Recognition based on DWT
and SVM”, Proc.Int’l Conf.On Wavelet Analysis and
Pattern Recognition, pp. 1382-1386,2007.
[13]. Sungjun Hong, Heesung Lee,Euntai Kim, “Fusion of
multiple gait cycles for human identification”, ICROS-
SICS International joint conference, pp.3171-3174,2009.
[14].L.R.Sudha, Dr.R.Bhavani“Performance Comparison
of SVM and KNN in Automatic Classification of Human
Gait Patterns”, International Journal Of Computers,Issue1,
Volume 6, pp.19-28,2012
[15].B. E. Boser, I. Guyon, and V. Vapnik. A Training
Algorithm for Optimal Margin Classifiers. In Proceedings
of the Fifth Annual Workshop on Computational Learning
Theory, pages 144-152. ACM Press, 1992.
[16]. C. Cortes and V. Vapnik. Support-Vector Network
Machine Learning, 20:273-297, 1995.