International Journal of Emerging Technologies and Engineering (IJETE) Volume 1 Issue 10, November 2014, ISSN 2348 – 8050

261

www.ijete.org

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

262

www.ijete.org

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

263

www.ijete.org

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

264

www.ijete.org

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.