2011 International Conference on Image Information Processing (ICIIP 2011)
Online Signature Verification using GA-SVM Jaspreet Kour
M.Hanmandlu
A.Q Ansari
EIE Department Galgotias College of Engg & Tech. Greater Noida, India
[email protected]
EE Department IIT New Delhi, India
[email protected]
EE Department Jamia Millia Islamia New Delhi, India
[email protected]
Abstract— This paper presents an online signature verification system based on Genetic Algorithm-Support Vector Machine (GA-SVM). The raw information, obtained from SVC 2004 database, as time functions is used to derive 75 features. Six different groups of features have been generated from 75 features and their performance evaluated using SVM. A method is proposed to reduce the computational complexity and the amount of memory required without compromising on accuracy using the sub set of features selected by Genetic Algorithm as the input to SVM. The experimental results show that this method provides good performance in terms of accuracy and memory requirement. Keywords- Biometrics, Online Signature, GA, SVM
I.
INTRODUCTION
Biometrics is an emerging field of technology. It makes use of unique but measurable physical, biological or behavioral characteristics to perform the identity verification of a person. Physiological biometrics is based on direct measurements of physical parts (such as fingerprint, face, iris, hand geometry etc.) of human body. Behavioral biometrics is based on the measurement of an action performed (such as signature, gait, speech, gesture etc.) by the individual [7]. The main advantage that signature verification has over other forms of biometric technologies is that signature is a well accepted biometric for identity verification in our society for years. The long history of trust of signature verification means that people are willing to accept a signature based biometric authentication system. But the drawback is that some people exhibit a lot of variability between different manifestations of their signature. Also signatures evolve with time and are influenced by physical and emotional condition of the signatories. Signature analysis is categorized into two modes: offline and online. In the offline signature verification, signatures are captured with a scanner or camera, saved and stored in digitized form for further processing whereas the online signature verification uses an electronic tablet and a stylus connected to a computer to extract information about a signature. It provides dynamic information like pressure, velocity, acceleration, number of strokes etc. Online signature verification is more robust, reliable and accurate than offline signature verification, as its dynamic
properties make forging of an online signature extremely difficult. Therefore online signature has become an attractive biometric method in the protection of small personal devices (PDA, small phone, laptop), in accessing sensitive data and building, and for the authentication of internet transactions such as e-commerce, e-banking, e-business, e-contract, etc. In this paper, a memory efficient online signature verification algorithm that uses the features chosen by Genetic Algorithm (GA) as input vector to Support Vector Machine (SVM) is presented. The online signature verification system uses only discriminating features selected by GA, which are unique and thus the memory requirement as well as the computational complexity are very less. The outline of this paper is as follows: Background for SVM and GA is given in Section II. Section III describes the proposed method to construct online signature verification system. In Section IV, the results obtained from different experiments are discussed. Section V gives conclusions and future scope. II.
BACKGROUND
A. Support Vector Machine Support Vector Machines (SVMs) create a margin between two classes, thereby facilitating an effective classification. Given the data containing two classes, SVM finds a hyper plane, which maximizes the distance from either class to the hyper plane and separates largest possible number of points belonging to same class on the same side of the hyper plane. Therefore misclassification error between the training set and the test set is minimized. Although in their basic form, SVMs learn linear threshold functions, but in nonlinear case, they can be used to learn polynomial classifiers, radial basis function (RBF) nets and multilayer perceptron by applying appropriate kernel functions. The dimensionality of the feature space has no direct relation to the learnability of SVM. In other words, SVMs judge the complexity of hypotheses underlying the classes according to the margin that separates them. Thus, even with large number of features, SVM pose no problem for their classification, provided they are separable using the functions from the hypothesis space [8].
Proceedings of the 2011 International Conference on Image Information Processing (ICIIP 2011) 978-1-61284-861-7/11/$26.00 ©2011 IEEE
2011 International Conference on Image Information Processing (ICIIP 2011) B. Genetic Algorithm Genetic algorithm (GA) is an adaptive and robust computational procedure, modeled on the pattern of natural genetic systems [5]. GA typically generates a constant population of candidate solutions to the optimization problems. Individuals are typically represented by n-bit binary vectors and the search space corresponds to n-dimensional Boolean space. The goodness of each candidate solution can be evaluated using fitness function. Evolutionary algorithms, using some form of fitness-dependent probabilistic method, select individual solutions from the current population to produce solutions for the next generation. Genetic operators are applied to selected individuals that constitute next generation. Mutation and cross-over are two common operators used in GAs. Mutation operator is applied on a single string to change its bits randomly. Crossover, on other hand operates on two parent strings to produce two offspring. The process of fitness depends on selection of input features and application of genetic operators to generate successive population of solutions until a satisfactory solution is found. III.
PROPOSED METHOD
Figure 1 shows the block diagram of the proposed online signature verification system. The whole process involves three steps: Feature Extraction, Features Selection and Verification. Training classifier
Feature Et
ti
Feature selection using GA
Figure 2. Azimuth angle and inclination angles of the pen with respect to the plane of graphic card
The raw data of each signature consists of the following information: •
Position on the x-axis
•
Position on the y-axis
•
Time stamp
•
Button status
•
Azimuth angle of pen with respect to the tablet (Fig 2)
•
Altitude angle of pen with respect to the tablet (Fig 2)
•
Pressure
A signature along with its raw dynamic features are shown in Figure 3 SVM classifier
Decision
Figure 1. Block diagram of proposed online signature verification system
A. Data Base The First International Signature Verification Competition was held in 2004, to provide a landmark on signature verification systems referred to as SVC2004 [10]. It contains 40 sets of signatures collected from different people and each set consists of 20 genuine signatures and 20 skilled forgery signatures. Signatures are acquired using WACOM Intuos tablet dynamically, when the instrumented pen moves on the tablet. Each signature is simply represented as a discrete time dynamic sequence. Figure 3. Example of signature(top) and its associated dynamic information(bottom)
Proceedings of the 2011 International Conference on Image Information Processing (ICIIP 2011)
2011 International Conference on Image Information Processing (ICIIP 2011) B. Feature Extraction Our aim is to find the most reliable and suitable features, as the discriminative power of features play a major role in the whole verification process. Using a set of raw dynamic data, 75 features have been derived as shown in Table 1. They represent a collection of some of the features that have been used, studied and reported in literature [2]. Six different groups of features generated include: shape, dynamics, time, velocity, geometry, and miscellaneous features [3]. TABLE I.
LIST OF FEATURES
Shape related features 1 2 3 4 5 6 710
11 12 13 14 15 16 17 18 19 20
21 22 23 24 25 26 27 28 29 30
Height to width ratio Length to width ratio
; ; Lw =
; LR =
Upper to lower side ratio Direction histogram S
; UL =
k=2.-----K ; l= 1,2,3,4 Dynamics related features Total Signature time ; T=tk-t1 Pen down time ; Td =
RMS speed
42 44 45 46 47 48 49
=
Pen down time Ratio
39 40 41 43
Horizontal mean-min diff; Ycn = Ym -min(Y(k)) Vertical mean-min diff ; Ycn = Ym -min(Y(k)) Left to right side ratio
31 32 33 34 35 36 37 38
; Tdr = Td / T
;
RMS – min- difference of speed ; Vm-min = Vmin(V(K)) Max-RMS- difference of speed`; Vmax-m = max(V(K)) – V Num of positive x velocity ; NV xp = card {Vx(K): Vx(K)>0} Num of negative x velocity ; NV xn = card {Vx(K): Vx(K)0} Num of negative y velocity ; NV yn = card {Vy(K): Vy(K)
