Signature Veri ﬁ cation Using Rough Set Theory Based Feature Selection

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Signature Veri ﬁ cation Using Rough Set Theory Based Feature Selection

Sanghamitra Das and Abhinab Roy

Abstract An offline signature verification system based on feature extraction from signature images is introduced. Varieties of features such as geometric features, topological features and statistical features are extracted from signature images using Gaborfilter technique. As all the features are not relevant, only the salient features are selected from the extracted one by a Rough Set Theory based reduct generation technique. Thus only the relevant features of the signatures are retained to reduce the dimension of feature vector so as to reduce the computation time and are used for offline signature verification. The experimental results are expressed using few parameters such as False Rejection Rate (FRR), False Acceptance Rate (FAR).

Keywords Gaborﬁlter

Feature extraction

Rough set theory

Feature selection

1 Introduction

Signature is considered to be one of the most widely accepted parameter for human identification. In this era of digital revolution, signature verification system is almost an‘inevitable application’not only in thefield offinancial transactions using credit/debit cards, cheque cashing but also for certificate verification, contract paper verification etc. In comparison with other identification technologies like face detection,fingerprint, retina, and so on, signature verification is more advantageous as an identity verification mechanism.

S. Das (&)

Department of Computer Science and Engineering, Hooghly Engineering and Technology College, Hooghly 712103, West Bengal, India

e-mail: [email protected] A. Roy

Department of Computer Science and Technology, IIEST, Shibpur, Howrah 711103, West Bengal, India

e-mail: [email protected]

©Springer India 2016

H.S. Behera and D.P. Mohapatra (eds.),Computational Intelligence in Data Mining—Volume 2, Advances in Intelligent Systems and Computing 411, DOI 10.1007/978-81-322-2731-1_14

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Signatures can be authenticated in two ways; either on-line or off-line. The signature image is scanned at a high resolution before giving it as input to the offline system. For online systems, the signature images are dynamically captured using devices like tablet, stylus, or digitizer.

Offline signature veriﬁcation system plays a pivotal role in identifying skilled forgery of signature against genuine signature. In case of online signatures, dynamic information makes the signature difﬁcult to forge. On the other hand, offline signatures are easier to imitate for an imposter.

This paper presents a novel set of features for the purpose of verification. Each signature image is preprocessed by some general preprocessing techniques and subsequently 2D-Gaborfilter is applied for feature extraction. As all the features are not equally important, only the salient features are selected from the extracted one by a Rough Set Theory (RST) based reduct generation technique andfinally few classification techniques are applied using the WEKA tool [1] to measure the effectiveness of the method. Experimental results show that the proposed technique provides better outcome compared to some previous verification techniques [2]

using the same GPDS (Digital Signal Processing Group corpus).

The paper has been divided inﬁve sections and accordingly discussed. Section2 describes Related Work. In Sect. 3 the proposed feature extraction and features selection method is described. The performance of the proposed method on experimental data set is discussed in Sect. 4. Finally conclusion and future perspectives are provided in Sect.5.

2 Related Work

Offline signature verification is a well known area where different features and classification approaches are used for signature authentication. For instance, in paper [3], grid-based feature extraction method employs information extracted from the signature contour and the verification is done using SVM classifier. Paper [4]

uses feature extracted from the local neighborhood of signature image. Cartesian and polar coordinate systems are used to divide the signature into zones and for each zone two separate histogram features are determined: (i) histogram of oriented gradients and (ii) histogram of local binary patterns. Support Vector Machines (SVMs) are used for signature classification. Two feature extraction techniques, Modified Direction Features and the Gradient features are compared in [2]. Both of them have used similar settings for their experiment. Moreover, performance comparison is made between squared Mahalanobis distance classifier and Support Vector Machines by utilizing the Gradient Features. However, in research work [5], two offline signature verification systems are proposed based on local and global approach respectively. The comparison has been done between the systems by employing a huge number of features encoding the orientations of the strokes applying morphology. For an improved signature verification system, features extracted on the basis of boundary of a signature and projections of the same are

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used in paper [6]. One of the features is obtained from total energy applied by a writer for their signature creation. Another feature highlights the ratio of the distance between key strokes of the signature image and the height/width of the signature image by using information obtained from the horizontal and vertical projections of an image. High pressure points obtained from a signature image (static) are considered as features for offline signature verification system and the extraction of same is depicted in paper [7]. A novel way is presented to calculate High Pressure threshold value from grayscale images. Image holding the high pressure points and the binary image of the primary signature is converted into polar coordinates where density distribution ratio of them is calculated. At the very end, two vectors are calculated to determine how far the points from geometric center of the original signature image. Robustness is tested for simple forgeries using KNN classifier. In [8], the co-occurrence matrix and local binary pattern are representing the features for signature verification and by applying statistical texture features the grey level variations in the image is calculated. For the training of a SVM model, genuine signatures and random forgeries have been used. Besides for the purpose of testing, random and skilled forgeries are utilized. Two key aspects of off-line signature verification are reported in [9]. One of them is feature extraction which produces a new graphometric feature set by considering the curvature of the essential segments of the signature. Here the shape of signature is simulated by applying Bezier curves and features are extracted from these curves. In the second aspect, for the improvement of reliability of classification based on graphometric features, an ensemble of classifiers is used. The graphometric feature set also les- sens the false acceptance rate. A feature set used in [10] illustrates the signature contour in accordance with spatial distribution of neighboring black pixels around a candidate pixel. Moreover, through the correlation among signature pixels, a texture feature is also calculated for offline signature verification.

3 Proposed Methodology

Initially, the image quality is improved by removing unwanted information from the collected signature images. Thereafter, binary image is inverted and median ﬁl- tering is done for noise reduction [11] and edge detection. Morphological thinning operation [12] is applied in the process of removing the selected foreground pixels from binary images. Thinning eliminates the variations of thickness of the signatures, because of age, illness, geographic location etc.

3.1 Feature Extraction

After preprocessing the signatures, some important local and global features are extracted.

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3.1.1 Feature Extraction Using Gabor Filters

Gaborfilters are bandpassfilters. The impulse response of Gaborfilter is formed by multiplication between a Gaussian function and a complex oscillation. As per the uncertainty principle Gabor Filters hold the optimal localization property in both the spatial and frequency domain. Moreover, Gaborfilters can be described using four parameters namely, standard deviation along x and y directions, frequency of the sinusoidal function, and orientation of thefilter. Thefilters focus on particular range of frequencies. The impulse response of Gaborfilter is defined using Eq. (1).

G xð ;yÞ ¼exp 1 2

x⁰ rx

2

þ y⁰ ry

2!!

expðið2pfx⁰ÞÞ ð1Þ

where,σxand σyare thery Standard Deviations along x and y direction f frequency of the sinusoidal function

x′ xcosθ+ysinθ y′ −xsinθ+ycosθ

θ Orientation of the Gaborﬁlter

The standard deviation along x and y axis determines the size of the Gaborﬁlter mask. The distinct values ofθ changes the sensitivity to edge and texture orientation. The variation infwill change the sensitivity to high and low frequencies.

3.1.2 Forming the Gabor Filter Mask

• The convolution mask of a Gabor ﬁlter bank is a coordinate plane where the rows are numbered fromrxtorxand the columns fromrytory.

• The values at each pixel position in theﬁlter mask is the value of the function G (x, y), where x and y are the x and y coordinate of that pixel position, with the given parameters.

• The Gaborﬁlter mask will contain an imaginary number at each pixel position.

• The complex mask is separated into real and imaginary parts and each part is convolved with the input image.

In proposed method two dimensional Gaborﬁlter with different values of the parameters called Gaborﬁlter bank is applied on input signature image shown in

Fig. 1 Input image to Gaborﬁlter

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Fig. 1. The pre-processed input binary image is convolved with each Gabor ﬁlter bank. Here, seven different orientations (0°, 30°, 60°, 90°, 120°, 150°and 180°) and four different frequencies (0.125, 0.25, 0.5, 0.75) are considered. Standard deviations and energies of the result image at each frequency for seven different orientations are calculated. Hence total 56 features (A₁–A₅₆) are obtained. Figures2,3,4 Fig. 2 Signature image after applying Gaborﬁlter bank at f = 0.125,θ= 180°

Fig. 3 Signature image after applying Gaborﬁlter bank at f = 0.25,θ= 60°

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and5are the four sample signature images that are convolved with Gaborﬁlter bank at different frequencies and orientations.

3.2 Rough Set Theory Based Feature Selection

The various concepts of rough set theory like discernibility matrix, core and attribute dependency are applied together to select the minimum number of important features, called reduct of the signatures. The work applies a reduct generation algorithm [13] that iteratively selects the locally most important feature andfinally provides a subset of relevant and important features which are sufficient to represent the signature verification system. The methodfirst separates the core and noncore features and selects one noncore attribute in each iteration until the reduct is found. The heuristic used in this approach is termed as forward attribute selection algorithm. The following algorithm is the rough set based algorithm [13] tofind out the important features of signatures where the notations have their usual meanings.

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4 Experimental Results 4.1 Experimental Database

This experiment uses a subset of GPDS-960 corpus. The subset consists of 10 sets where each set consists of 24 genuine signatures and 30 high skilled forgeries.

Naturally this experiment involves 240 genuine signatures and 300 high skilled forgeries. The signatures are in“bmp”format, in black and white color and 300 dpi.

4.2 Performance Assessment

The sample conﬁguration of the features selection with their accuracies calculated by the proposed algorithm (FSFS) for different classiﬁers is summarized in Table1.

The results of our experiments using different features are given in Table2.

Table 1 Sample reducts and accuracies obtained by forward selection

Classiﬁer R1 R2 R3 R4 R5

Naïve bayes 95.83 93.33 95.83 94.16 95.83

Logistic 94.16 91.66 95.83 95 95.83

BayesNet 90.83 89.16 92.5 90 91.66

KStar 95 95 95 95 94.16

ClassiﬁcationVia Regression

94.16 96.66 93.33 95 97.5

Bagging 88.33 90 91.66 89.16 95

Decorate 90 90.83 89.16 89.16 91.66

MulticlassClassiﬁer 95.83 92.5 95 93.33 94.16 Hyperpipes 88.33 85.83 89.16 86.66 90.83

DTNB 86.66 91.66 90.83 89.16 91.66

FT 97.5 96.66 97.5 97.5 96.66

FilteredClassiﬁer 87.5 87.5 89.16 88.33 87.5

RepTree 89.16 86.66 90.83 91.66 88.33

RandomForest 94.16 90 90.83 92.5 95

Table 2 Experimental results of FAR and FRR using FSFS algorithm for different classiﬁers

Feature: Gaborﬁlter based features

Classiﬁer Feature Selection Algorithm (FSFS)

FAR (%) FRR (%)

FilteredClassiﬁer 13.33 11.67

Bagging 20 10.84

RepTree 20 10

RandomForest 20 7.5

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Table 2 gives the results obtained with the selected features by the proposed system on GPDS-960 dataset. We have used classiﬁers using 10 reference signatures. 24 genuine signatures are used in training purpose and 30 skilled forgeries are used in testing purpose.

5 Conclusion and Future Perspectives

In this paper Gaborfilter based feature extraction technique and rough set theory based feature selection algorithm is applied on signature image to retain only the important features for offline signature verification system. Several existing classifiers are applied for performance analysis. The experimental results show that, the accuracies given by various classifiers are comparable with other popular existing methods. Though the FAR and FRR values in Table2 show that the system can accurately identify the signatures but it is not so helpful in case of forgery detection, which is our main concern as future work.

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