Rapid Off-line Signature Verification Based on Signature Envelope and Adaptive Density Partitioning Vahid Malekian
Alireza Aghaei
Mahdie Rezaeian
Mahmood Alian
Department of Biomedical Engineering Amirkabir University of Technology Tehran, Iran
[email protected]
Pazhuheshgarane Systemahye Hooshmandes Sepano Co. Isfahan Science and Technology Town Isfahan, Iran
[email protected]
Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan, Iran
[email protected]
Department of Computer Islamic Azad University of Qazvin Qazvin, Iran
[email protected]
Abstract— Handwritten signature is a widely used biometric which incorporates high intra personal variance. The most challenging problem in automatic signature verification is to extract features which are robust against this natural variability and at the same time discriminate between genuine and fake samples. This paper presents a novel method for extracting easily computed rotation and scale invariant features for offline signature verification. These features are extracted using the signature envelope and adaptive density partitioning. The effectiveness of the proposed features has been investigated over 900 signatures using a neural network classifier. The experimental results show the verification accuracy rate of 90.7%. Keywords— Offline Signature Verification, Signature Envelope, Adaptive Density Partitioning, Artificial Neural Network (ANN)
I.
INTRODUCTION
andwritten signature is a quick, simple, convenient and cost effective behavioral biometric. Automatic Signature verification has a variety of applications in personal identification, bank cheque validation, Automatic Teller Machine (ATM) access and similar automatic verification problems. The objective of this problem is to recognize handwritten signature inimitably to prevent fraud and ensure that account mandates are upheld. The combination of multifarious penmanship with various geometric patterns makes signatures unique, relatively hard to be guessed and fairly reliable means for authentication. This partly explains why handwritten signature is still the most commonly used and widely accepted official mean to confirm client permission on legal document such as cheques, credit cards and contracts. The huge number of the financial and banking institutions, growing number of transactions and development of electronic commerce demand a move toward robust and accurate signature verification systems [1]. Automated handwritten signature verification can be performed either online or offline. Online or dynamic
978-1-4673-6206-1/13/$31.00 ©2013 IEEE
approaches analyze hand speed, the sequence of pen stroke, trajectories and pressure measurements while signature is being written. In contrast, offline or static signature verification employs a pre-recorded static image of the signature and consequently obviates the necessity of any special devices or the presence of the signature owner[2]. Although offline verification is more challenging, it is already popular and has more potential applications. During the last few years, researchers have made great effort on signature verification. In the offline category, Nguyen et al. [2] proposed global features based on the boundary of signature and its projections. In [3][3] Nguyen and Blumenstein described a grid-based feature extraction technique that utilizes directional information by applying a suitable 2D Gaussian filter. Ferrer et al. [4] presented a set of geometric features based on the description of the signature envelope and the interior stroke distribution in polar and Cartesian coordinates. Prakash and Guru [5] offered a method of offline signature verification based on the bi-interval valued symbolic representation. Bansal et al. [6] used a graphmatching based automatic signature verification technique which is based on geometrical shape of the critical regions of the signature. The main aspects related to the process of signature verification are discussed in [7]and [8]. In this paper, a novel approach for offline signature verification has been proposed. The presented method is based on six powerful features in combination with a neural network classifier. The novelty of the system lies mainly on the feature extraction method. The rest of the paper is organized as follows. Section II describes in detail the major steps of our proposed method that includes preprocessing, feature extraction and classification. Results are presented in Section III. II.
METHODOLOGY
We approach the problem in three steps. Initially the scanned signature image is pre-processed to be prepared for extracting features. The output image is used to extract features
that can distinguish signatures of different persons. Finally, signature is verified by matching extracted features against those stored in the database. Fig.1 depicts the configuration of our signature verification system. In the following, each part is briefly discussed.
Fig. 3. Binarized image (a) and its complement(b).
3) Signature Dilation Dilatation operation is applied to compensate small existing gaps along the signature resulting from the signature variability or binarization. Fig.4 shows an example of the dilated image.
Fig. 4. Image before (left) and after (right) dilation. Fig. 1. Block diagram of the proposed verification system.
A. Preprocessing Some preprocessing steps have to be applied to raw signature images. The purpose of the preprocessing phase is to make input image ready for further feature extraction. We assume that each signature is written on a white piece of paper. The input image is a gray-scale image containing a scanned handwritten signature. In this paper, we applied a three-step preprocessing procedure, which includes noise removal, binarization and dilation. 1) Noise Removal The scanned signature image contains salt and pepper noise. The image is passed though a median filter to eliminate noise inherent in acquisition process. The result is illustrated in Fig.2.
Fig. 2. Noise suppression.
2) Binarization and Complementation The next step is converting gray scale image into black and white. As shown in Fig. 3a in the binarized image background is white and the signature is black. To simplify the subsequent steps we compliment this image. The complement image contains a white signature foreground in a black background (Fig. 3b).
B. Feature Extraction The output from the preprocessing stage is given as input to the feature extraction stage. Feature extraction is the process of extracting essential discriminative information and representing it in a suitable form for the classifier. Similar to many other pattern recognition problems, feature extraction is a crucial step which contributes significantly to the overall performance of an offline signature verification system. Feature extraction technique must be tolerant to different types of variation whilst preserving essential information of input patterns. We used a set of six features to uniquely characterize a candidate signature. These features are based on the shape and dimensions of the signature image. The features that we employed are: 1) Aspect Ratio The horizontal distance measured between the first and the last signature point captured in the X direction or simply the length of the enclosing rectangle, varies for different individuals. But it is not stable among signature scaling. To overcome this problem signature length is normalized to signature width. The aspect ratio ( A ) is the ratio of length to width of the signature bounding box (Fig.5) [7]. It remains invariant to scaling. If the user signs in a different size, the height and length will be altered proportionally to retain the aspect ratio. A=
L W
(1)
5) Envelope signals obtained based on the center of gravity Envelope signals are computed by means of the filled outlines of the signature (Fig. 6).
Fig. 5. Signature bounding box.
2) Horizental to diagonal length ratio The next feature is the ratio of signature length to the length of the diagonal line of the bounding box. R=
L D
(2)
L and D have been shown in Fig.5.
3) The slope of the line joining the center of gravity and geometric center This feature is the angle between the horizontal axis and the vector connecting the center of gravity to the geometric centre of the bounding box. The center of gravity formula is given in (3). N is the number of signature pixels (white pixels) in binary image.
N
Fig. 6. Horizontal and vertical filled outline of the signature.
Vertical envelope signal is the number of white (one) pixels in each column of the vertical filled outline of the signature. Similarly, horizontal envelope signal is the number of white pixels in each row of the horizontal filled outline of the signature. Fig. 7 demonstrates vertical and horizontal envelope signals obtained from the signature outline in Fig. 6.
N
∑ xi ∑ yi
G = ( x g , y g ) =( i = 1
,i =1
N
N
(3)
)
The angle between the center of gravity and geometric centre ( α ) is given by:
α = tg
G
x g, y g
y −y −1 o
g
As Fig. 8 shows, any rotation of the input signature changes the outline of the signature. Accordingly, envelope signals are not robust against rotation.
(4)
xo − x g
is the centre of gravity and
o
Fig. 7. Vertical (left) and horizontal (right) envelope signals.
x o, y o
is the
geometric centre of the bounding box. 4) Vertical and horizontal variance of signature Vertical and horizontal variance of signature can also be used as distinctive features for verification: N ∑ ( xi − x g ) 2 i =1 σ 2x = N N ∑ ( yi − y g)2 i =1 σ 2y = N
Fig. 8. The effect of 30° rotation on the vertical (left) and horizontal (rigth) filled outlines.
(5)
To achieve rotation invariance, the outline of the signature is obtained using the center of gravity. For this, the outermost pixels of the signature are joined to its center of gravity. (5)
Fig. 9. Signature filling using the center of gravity.
The X-axis of the extracted envelope signals represents the number of circumferential pixels in the filled image. This determines the length of the feature vector and may vary for different signatures. Before the signals are fed into the classifier, they should be quantized to have the same length. Given the number of quantization levels, one way is to divide the X-axis by the number of quantization levels and to find maximum, minimum, mean or even a random value of the envelope signal in each interval. We set the number of quantization levels to be 30. Each element of the feature vector is the mean of the corresponding interval of the envelop signal. Furthermore, all feature vectors are normalized to minimize the effects of the size and other changeable characteristics of image. Fig. 10 shows the quantized and normalized envelop signals for six different signatures.
Fig. 10. Envelope signals obtained based on the center of gravity.
6) Signature density around the center of gravity Adaptive density partitioning is the other novel and effective feature extraction technique presented in this paper. In this method, the preprocessed image is split into four partitions through its center of gravity. Each quadrant is then divided into four equal parts. This way signature is split into sixteen segments. The procedure of adaptive density partitioning has been illustrated in Fig. 11. The feature vector is constructed from local density of signature in each segment. Local density is defined as the ratio of the number of signature (white) pixels to the area (total number of pixels) in each segment. This yields a discriminative scale invariant feature vector of length 16.
Fig. 11. Adaptive density partitioning: G is the centre of gravity.
C. Classification After the feature extraction, a classifier is built on top of the features. To perform classification task for signature verification, machine learning techniques such as Artificial Neural Networks (ANN), Support Vector Machines (SVM) [10], or a threshold decision method [4] can be employed. For more than half a century, ANN has been a fundamental part of pattern recognition. ANN allows a capability of modeling complex functions and its application is easy as well [11]. An ANN is typically characterized by its architecture and its learning process. 1) ANN Architecture The architecture of an ANN is referred to as the interconnection structure between different layers of neurons. Multi-layer perceptron (MLP) neural network has been among the most widely satisfactorily applied classifiers for handwritten signature verification problems[12]. We applied a MLP with one hidden layer. The number of hidden layers depending on the complexity of functions and input features is selected via trial and error. Most of the time using more than two hidden layers does not improve the performance and only increases calculation cost [13]. The number of neurons on the input layer is 51 which is obtained by adding the length of all the six extracted features. The number of hidden neurons can theoretically be any number, however as the network complexity increases, there is an increased chance of the network overfitting the training data and the classifier losing the ability to generalize [14]. According to our experience it is better to choose the number of hidden neurons near half the input neurons. Hence it was set to be 30. The number of output neurons depends on the number of classes which for verification problem is two including genuine and fake. The activation function is log-sigmoid for hidden layer and linear for output layer. The activation function defines the output of neurons in terms of their weighted inputs. 2) Training ANN An ANN requires training to learn an unknown inputoutput relationship to solve a problem. The MLP network trained using a supervised learning technique called Levenberg-Marquardt back-propogation (trainlm) that adjusts the network weights according to Levenberg-Marquardt optimization. The Levenberg–Marquardt updating rule is more efficient than the delta rule (sum-squared error minimization) which is used in regular standard back-propagation [15]. "Trainlm is often the fastest back-propagation algorithm in the MATLAB neural network toolbox, and is highly recommended as a first-choice supervised algorithm"[16].
Training occurs through an appropriate database of signatures. Due to the unavailability of a standard database, we gathered our own database. This database contains 900 signatures collected from 45 people. Since generally signatures signed by the same person may vary in different trials, each individual was asked to provide 10 samples in multiple sessions over up to 2 weeks period. Trainlm as a supervised learning method needs forgery samples in the training set. Thus, in addition to the genuine samples, 10 forged specimens for each person were collected. Fig. 12 presents some examples of the signatures in the dataset.
TABLE I.
ACCURACY OF THE METHOD. Number of samples
FAR
FRR
FCR
Recall Test
600
0.47%
0.36%
0.83%
Generalization Test
300
5.3%
4.0%
9.3%
The overall correct classification rate of the proposed method is 99.17% for the training set (recall) and 90.7% for the test set (generalization). The best, worst and average response time in MATLAB (Release 2008) on a laptop computer with Intel core i5 processor, 2.27 GHz and 4 GB RAM running Windows 7 are presented in Table II. TABLE II.
Generalization Test
RESPONSE TIME OF THE METHOD.
Best time
Worst time
Mean time
1.7 s
3.2 s
2.2s
According to Table II, this method has a high speed for offline signature verification. This computation speed should be acceptable for real applications. IV.
Fig. 12. Sample signatures.
III.
RESULTS
To check the generalization capability of the network, the database was separated into two subsets: training and testing. The training set comprised of 300 genuine and 300 fake signatures was used to train the network. The remaining 300 samples consisted of 150 genuine and 150 fake signatures were allocated to the test set. The latter was not used in the network training. Once trained, both training and test sets were tested with the network. In the recall test, trained samples were classified to check the learning proficiency and convergence of the network. In the generalization test, the ability of the network to recognize new untrained samples was assessed. The results obtained in the recall and generalization tests are summarized in Table I. For each test three types of errors are reported: false acceptance rate (FAR) as the percentage of the accepted fake signatures, false rejection rate (FRR) as the percentage of the rejected authentic signatures and false classification rate which is the summation of these two errors. Among 150 genuine signatures, from 45 different persons, presented to the network 144 signatures were classified as genuine and 6 signatures as forgeries. Thus FRR of the system is 4.0%. Out of the 150 forged signatures tested with the network, 8 signatures were classified as genuine and142 as forgeries. Thus FAR of the system is 5.3%.
CONCLUSION
In this paper a novel method for offline signature verification was presented. The novelty of the method lies mainly on the proposed techniques for extracting rotation and scale invariant features. The performance of the features in conjunction with four other simple global features has been investigated using neural network. The combination of discriminative features with the use of Levenberg–Marquardt algorithm for learning the neural network made the proposed system efficient in terms of verification accuracy, simplicity and run time. REFERENCES [1] [2]
[3]
[4]
[5]
[6]
[7]
A.K. Jain, F.D. Griess, S. D. Connell, "On-Line signature verification", Pattern Recognition 35 2963 – 2972,2002. V. Nguyen, M. Blumenstein, and G. Leedham, "Global features for the off-line signature verification problem", in ICDAR. IEEE Computer Society, pp. 1300–1304, 2009. V. Nguyen, M. Blumenstein, "An application of the 2d gaussian filter for enhancing feature extraction in off-line signature verification", in ICDAR. IEEE Computer Society, pp. 339–343, 2011. Miguel A. Ferrer, Jesu´s B. Alonso, and Carlos M. Travieso, "Offline geometric parameters for automatic signature verification using fixedpoint arithmetic", IEEE Transactions on Pattern Analysis and Machine Intelligence, VOL. 27, NO. 6, JUNE 2005. H.N. Prakash, D.S. Guru "Offline signature verification: an approach based on score level fusion", International Journal of Computer Applications (0975 – 8887), Volume 1 – No. 18, pp.52-58, 2010. A. Bansal, B.Gupta, G. Khandelwal, and Sh. Chakraverty, "Offline signature verification using critical region matching, " International Journal of Signal Processing, Image Processing and Pattern Vol. 2, No.1, March, 2009. G.Dimauro, S.Impedovo, M.G. Lucchese, R. Modugno and G. Pirlo, "Recent advancement in automatic signature verification," Proceedings of 9th International Workshop on Frontiers in Handwriting Recognition (IWFHR), pp. 179- 184. 2004.
[8]
[9]
[10]
[11]
[12]
[13] [14] [15]
[16]
M.S. Arya , V.S.Inamdar “A preliminary study on various off-line hand written signature verification approaches” International Journal of Computer Applications , Volume 1 – No.9, pp. 0975 – 8887 , 2010. A. McCabe, J. Trevathan and W. Read, "Neural network-based handwritten signature verification," JOURNAL OF COMPUTERS, VOL. 3, NO. 8, AUGUST 2008. E. Ozgunduz, T. Senturk, E. Karsligil, "Off-Line signature verification and recognition by support vector machine," in: 2005 European Signal Processing Conference, 2005. A.McCabe, J.Trevathan and W.Read “Neural network-based handwritten signature verification” Journal Of Computers, Vol. 3, No. 8, August 2008. H. Baltzakisa, N. Papamarkosb,"A new signature verification technique based on a two-stage neuralnetwork classifier," Engineering Applications of Artificial Intelligence 14, 95-103, 2001. D. Pattreson, Artificial Neural networks - Theory and applications, Prentice Hall, 1996. M. T. Hagan, H. B. Demuth, and M. H. Beale, Neural network design. boston: PWS Publishing Company, 1996. Zayani, R., Bouallegue, R., Raviras, D., “Levenberg-Marquardt learning neural network for adaptive pre-distortion for time varying HPA with memory in OFDM systems”, EUSIPCO, 2008. http://www.mathworks.com/help/tooolbox/nnet/ref/trainlm.html
