JED’10
Annaba, ALGERIA, June 14-15, 2010
An Off-Line Signature Verification System Based on Retinal Coding and SVM Nassim Abbas1, Youcef Chibani1, Mohamed Cheriet2 1
Speech Communication and Signal Processing Laboratory, University of Science and Technology Houari Boumediene (USTHB), BP. 32, El Alia, Bab Ezzouar, 16111, Algiers, Algeria :
[email protected],
[email protected] 2 ETS - Ecole de Technologie Supérieure, 1100, rue Notre-Dame Ouest - Montréal (Québec) H3C 1K3 – Canada :
[email protected] Abstract—The objective of this work is to present an off-line signature verification system based on Retinal Coding (RC) and Support Vector Machines (SVM), which is basically divided into two parts: i) feature extraction using RC and ii) classification using SVM. Retinal coding process applied on the pre-processed off-line signatures is used for obtaining the same optimal length of the feature vectors for each writer without using width normalization on the original images and SVM classifier is used to obtain the maximum performance quality of the verification process about whether the signature is genuine or forgery. A learning process based on SVM is exploited, where the aim is obtaining the best model that is able to represent each writer's signature. Experiments are conducted on the well know on GPDS database using false rejection and false acceptance criteria. The obtained results show the effectiveness of this approach compared to previous methods. Keywords: Off-line signature verification, Retinal coding, Support vector machines, Genuine signatures, Skilled forgeries.
1. INTRODUCTION The handwritten signature is the biometric modality the most accepted by many peoples. Hence, its use is more appropriate for the verification on a system. Generally, the Handwritten Signature Verification System (HSVS) is often categorized in two major classes: on-line HSVS and off-line HSVS. The on-line HSVS data are captured using an electronic tablet and other devices. In contrast, the off-line HSVS images of the signatures written on a paper are captured using a scanner or a camera [1]. Off-line signature processing remains important since it is required in office automation (OA) systems as well as for the validation of cheques, credit cards, contracts, historical documents, etc. Signatures are a special case of handwriting in which special characters and flourishes occur and therefore most of the time they can be unreadable. Also intrapersonal variations and interpersonal differences make it necessary to analyze them as complete images and not as letters and words put together [1]. Signatures are subject to three types of forgery, random, simple and skilled. Random forgeries are formed without any knowledge of the signer’s name and signature’s shape. Simple forgeries are produced knowing the name of the signer but without having an example of signer’s signature. Therefore, random and simple forgeries are easily identified. Skilled forgeries are produced by people looking at an original instance of the signature, attempting to imitate as closely as possible. Therefore, the skilled signature is very similar to the original one, making it much more difficult to verify the forgery [2]. Usually, the HSVS is composed of three modules: preprocessing, feature extraction and classification [21]. The preprocessing module consists to make signatures standard and ready for feature extraction, which is generally composed by several steps: Background elimination, noise reduction, width normalization, skeletonization, etc. The extraction of features from each signature is a necessary step in the classification process, as features help to uniquely differentiated signatures from each other. Features extracted from static signature images can be classified as global or local features. Global features describe an entire signature and include the discrete Radon Transform [3], the Hough transform [4], the discrete Wavelet transform [5], the Contourlet transform [6], horizontal and vertical projections [7], and smoothness features [8]. Local features are extracted at stroke and substroke levels and include unballistic motion and tremor information in stroke segments [9], stroke “elements” [7], local shape descriptors [10] and slant features [11]. Finally, the classification module allows performing the decision about whether the signature is genuine or forgery. Several classification methods have been proposed as template matching techniques [5], [7], [9], minimum distance classifiers [8], [10], support vector machines [12], [13], hidden Markov models (HMMs) [14], [12], [3], [13], neural networks [4], [11], [15], [1]. Arif and Vincent (2003) concerned data fusion and its methods for an off-line signature verification problem which are Dempster-
Shafer evidence theory (DST), possibility theory and Borda count method [16], [23]. Ismail and Gad [17] and Quek and Zhou [11] proposed some algorithms for signature verification based on fuzzy modeling. Sansone and Vento (2000) increased performance of signature verification system by a serial three stage multi-expert system [2]. In this paper, we propose an off-line HSVS using the Retinal Coding (RC) [25] and Support Vector Machines (SVM). The RC allows extracting locally features without normalization of the signature image. On each part of signature, the densities are computed providing overall signature appearance information. In contrast, the SVM allows separating optimally the genuine from the forgery signatures. The paper is then organized as follows. Section 2 presents the description of proposed verification system. Experiments conducted on the GPDS database are presented in section 3. The last section gives a summary of the proposed method and looks to the future research direction.
2. DESCRIPTION OF THE SYSTEM The structure of our system is shown in Figure 1. The descriptions of the preprocessing, feature extraction and classification process of signatures are presented in the following subsections. Data Acquisition
Images
Preprocessing
Feature vectors
Images
Feature Extraction
Classification
Output (Decision)
Fig.1. The structure of the verification system 2.1. Preprocessing Any image-processing application suffers from noise like touching line segments, isolated pixels and smeared images. Hence, the acquired image should be processed in order to facilitate the feature extraction. In our application, we use the processed signature without unifying size image for verification process. Since some papers have discussed pre-processing [20], [21], and this problem is not our focus in this paper, we do not introduce our methods on pre-processing in details. In what follows, we shall describe how features are extracted from a signature image by using the retinal coding method. 2.2. Feature extraction The retinal coding consists to place a virtual grid of fixed size on the image and determine for each cell of the grid the ratio of pixels belonging to the signature. There is different methods to achieve retinal coding. We propose a simple procedure which is easy to implement. The Figure 2 illustrates this procedure which can be decomposed into three steps: • The initial image of size 𝑛 × 𝑚 is centered in a square matrix of dimensions 𝑚𝑎𝑥(𝑛, 𝑚) × 𝑚𝑎𝑥(𝑛, 𝑚) . • In order to ensure that the virtual grid falls tight, the image is enlarged so that its size becomes 𝑙 ∗ 𝑚𝑎𝑥(𝑛, 𝑚) × 𝑟 ∗ 𝑚𝑎𝑥(𝑛, 𝑚), where 𝑙 and 𝑟 are the number of the row and column of the retina, respectively. • From each area the local characteristic is defined as the ratio between the surface signature and the surface of the area. Finally, the obtained values are stored in a feature vector of size 𝑙 ∗ 𝑟 × 1 , which characterizes the signature image.
1.0000 0.9799 0.9996 0.9285 0.8206 0.8275 0.9952 1.0000 0.9829
𝑛×𝑚
𝑚×𝑚
3𝑚 × 3𝑚
3×3
9×1
Fig.2. Illustration of the used procedure to perform retinal coding Figure 3 depicts the use of various size of the retina. We can note that a size 5 × 5 of the retina allows an important reduction of the representation vector, but it preserves wrongly the visual information. In contrast, a 20 × 20 retina which provides an accurate representation of images, but it leads a larger characteristic vector. A 13 × 13 retina seems to be an optimal choice between the
quality of representation and dimensionality. Thus, the optimal choice of retina size for each writer is obviously too important to effectively solve our problem of signature verification.
5×5
13 × 13
20 × 20
Fig.3. Visualization of obtained results with different dimensions of retinas for the writer 61 2.3. Classification The classification based on Support Vector Machines (SVMs) has been used widely in many pattern recognition applications as the handwritten signature verification [12], [21]. The SVM is a learning method introduced by Vapnik et al. [18], which tries to find an optimal hyperplane for separating two classes. Its concept is based on the maximization of the distance of two points belonging each one to a class. Therefore, the misclassification error of data both in the training set and test set is minimized. Basically, SVMs have been defined for separating linearly two classes. When data are non linearly separable, a kernel function is used as polynomial function, radial basis function (RBF) or multi layer perceptron. The classification based on SVMs involves training and testing stages. The training stage consists to find the optimal parameters. Hence two parameters should be determined: the kernel parameter and the regularization parameter. These two parameters are found experimentally depending on the dataset. The testing stage allows evaluating the robustness of the signature verification system. Figure 4 depicts the flow chart for training and testing the signature verification system, where 𝑁 is the number of training examples.
3. EXPERIMENTAL RESULTS
Training Stage
Testing Stage Test data
Learning data
𝑖←0 Input Signature 𝑖 ←𝑖+1
Signature (i)
Pre-processing
Pre-processing
Feature Vector Extraction (Optimal length)
Feature Vector Extraction (Optimal length)
No 𝑖=𝑁 Yes Storage of the SVM model
Validation SVM Verification Result
3.1. Data description The Grupo de Procesado Digital de Senales (GPDS) signature Fig.4. The flow chart of proposed algorithm dataset is a database that has been widely used in literature . [22], [23], [21]. This dataset consists of 300 signature sets, where each set has been composed by one writer, who provided 24 samples of their signature for the genuine component of the dataset. The forgeries were written by three forgers, each of whom were allowed to practise the signature for as long as they wish, with these forgers then imitated five genuine signatures three times. This produced 30 skilled forgeries for each set, which came from 10 forgers. In total, the GPDS dataset contains 16200 signatures, which is built from 7200 genuine signatures and 9000 forgeries. Figures 5 and 6 show 4 examples of both the genuine and forgery signatures for one writer respectively.
Fig.5. GPDS genuine samples for one writer
Fig.6. GPDS forgery samples for the same writer in Figure 5
3.2. Training stage Since SVM supports only two-class verification, a bi-class system can be constructed by using some samples of both the genuine and forgery signatures for each writer respectively. Thus, the Sequential Minimal Optimization (SMO) algorithm is used for training SVM [24].
In the training stage, 18 positive (genuine) and 23 negative (forgery) examples are selected for each person. The 23 skilled forgeries were written by three forgers. Each example includes 𝑟𝑜𝑝𝑡 × 𝑟𝑜𝑝𝑡 extracted grid features which are normalized into 0, 1 . Such as, the parameter 𝑟𝑜𝑝𝑡 represents the optimal retina which is adapted for each writer. Possible kernel options are linear, polynomial, radial basis function and sigmoid. In this system radial basis function is used which gave the best results. 3.3. Verification results and discussion In this system for each person 6 original and 7 forgery (skilled forgeries) signatures are tested. In verification phase the same 𝑟𝑜𝑝𝑡 × 𝑟𝑜𝑝𝑡 features are used. Different experiments were developed for each writer in order to find the optimal length of the retina. Hence, the error rate will be the average between false rejection and false acceptance rates for some specific threshold obtained in the training phase. The error rates depicted in Figure 7 are computed based on the writer 61. As we can observe, the best classifier is the one obtained with an optimal length of retina equal to 13. Each writer has its own optimal retina; therefore, it can’t be shown a Receiver Operative Curve (R.O.C.) for all this system. Three different kinds of error have been calculated: the error rate taking into account only genuine signatures that are false rejected (FRR); the error rate taking into account only skilled forgeries that are false accepted (FAR); and the average error rate taking into account both cases (AER). The experiments for each writer can be split in two steps, first, searching of the best training model (with kernel RBF and SMO algorithm). When the best model is found, the next step will be the searching of the optimal retina. The detail result on the test signatures which belongs to 11 writers is shown in Table 1. Thus, the average on the three kinds of error has been respectively calculated for the 10 first writers. As it can be seen in Table 1, the results shown that the best signature verification result for each writer depends both on the choice of the regularization constant 𝐶 which trades off wide margin with a small number of margin failures, the choice of the standard deviation 𝜎 when working with a kernel RBF and finally the choice of the optimal length retina 𝑟. Table 1. Obtained results using a set containing 11 writers for the test Writer Id
𝝈
Parameter 𝑪 𝒓
1 2 3 4 5 6 7 8 9 10 61
2.5 4.3 4.6 1 2.5 3.1 2.2 1.6 2.5 2.2 6.4
15.1 3.1 4.1 4.1 7.1 4.1 2.1 4.1 1.1 12.1 1.1
4 5 3 3 5 6 4 3 10 3 13
Average of 10 first writers
Error Rate (%) 𝑭𝑹𝑹 𝑭𝑨𝑹 𝑨𝑬𝑹 16.6 0 0 0 16.6 0 0 0 16.6 16.6 0
14.2 0 14.2 0 0 0 0 0 0 0 0
15.3 0 7.6 0 7.6 0 0 0 7.6 7.6 0
6.66
2.85
4.61
Fig.7. Performance evaluation for the writer 61 using different retina values
After the Experiments, we get the false rejection rate as 6.38% and false acceptance rate as 2.33% for all the 300 writers of GPDS database. A comparison between our signature verification results and previous method is shown in Table 2. Table 2. Performances for signature verification (results are given as indicated in [21]) Verification approaches SVM+Structural [21] SVM+Gradient [21] SVM+RC (Our approach)
Average Error Rate (%) 6.26 5.94 4.20
4. CONCLUSION AND FUTURE WORK We proposed and presented a new system for the verification of static or off-line signatures. The verification technique used in this system is based on retinal coding features of signatures and SVM. The results show that choice of learning model parameters and length of retina which is adapted for each writer is important but not difficult. Thus, we need to make a proper choice on the optimal retina in order to obtain a suitable performance quality on the off-line signature verification. Carefully chosen discriminating retinal coding features of signatures combined with the use of SVM made our system more powerful compared to other existing systems both in terms of success ratio and ease of implementation and optimized run time. In continuation to the present work, the next objectives are: to incorporate new global, mask and grid features to improve performance quality of the verification process about whether the signature is genuine or forgery, in the false rejection and false acceptance concept.
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