FAST SIGNATURE VERIFICATION WITHOUT A SPECIAL TABLET
P. MAUTNER AND O. ROHLIK AND V. MATOUSEK University of West Bohemia, Faculty of Applied Sciences, Univerzitni 8, CZ – 306 14 Plzen (Pilsen), Czech Republic e-mail: mautner | rohlik |
[email protected] J. KEMPF University of Technology (Fachhochschule) Regensburg, Pruefeninger Straße 58 D – 93049 Regensburg, Germany e-mail:
[email protected]
Signature verification is a method frequently used for personal identification. In this paper we describe signature verification using the special digital data acquisition pen. The pen produces three signals in which information about the pressure applied to ballpoint and about the side pressure in x and in y directions is involved. These signals are processed by fast wavelet transform and the feature vector representing the signature is obtained from the wavelet coefficients. The ART-2 neural network model is used for signature verification. Architecture of the verifier and achieved results are discussed here and ideas for future research are also suggested.
1. Introduction There are many commercial systems designed for person identification worldwide. Among the most popular ones are those based on fingerprints, ID cards and signature recognition using optical character recognition (OCR) methods. For the purpose of signature verification a special digital data acquisition pen was developed at the University of Technology Regensburg. Detailed information about an acquisition device is given in Section 2. Section 3 deal with the pen output signal feature extraction method and Section 4 describes the neural network verifier based on the unsupervised learned neural network model of ART- 2. Results of verification experiments, and possible future works are discussed in Section 5 and Section 6, respectively. 1
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a) Digital data acquisition pen b) transformed output signal waveforms Figure 1.
Figure 2.
ART-2 signature verifier
2. DATA ACQUISITION DEVICE As was mentioned above signature verification is performed by the special digital data acquisition pen. The first experimental pen was built at University of Technology Regensburg during the spring of 2000. Our experiences with this pen were discussed in [1]. The latest pen prototype (illustrated at Figure 1a) consists of three sensors integrated in the pen producing signals in which information about the pressure applied to ballpoint and about the side pressure in x and in y directions is involved. To obtain the right signal waveform, the pen should be held so that the x side sensors are parallel to the writing axis and the y sensors are perpendicular to it. Failing that, the signals corresponding to the x and y side pressure are distorted. In order to reduce this effect, these signals are transformed to polar coordinates mag and phi. The transformed output signal waveforms are illustrated in Figure 1b). 3. FEATURE EXTRACTION Before the feature vector is evaluated from the output signals, only the active part of the signature has to be determined. This is done from the first difference of output signal z. For the extraction of features from signals, the fast wavelet transform (FWT) is used [2]. In our application the Daubechies and Coiflet wavelet families were tested for decomposition, the 5-th order Daubechies wavelet gave the best result. Using of this wavelet, the following features were used to describe the signature:
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• rl = sl n , where sl is the number of samples of the signature and n is the total number of samples in one scan (n = 500 for all tested signatures). Nm X S • Dm = d2mj , m = 1, 2, ..., 5; s = x, y, z, j=1
where Nm is the number of coefficients in scale m. N5 X • AS5 = a25j , m = 1, 2, ..., 5; s = x, y, z, j=1
where N5 is the number of coefficients in scale 5. s Features AS5 and Dm represent the energy of the m-th signal decomposition level, rl represents the relative length of the active signature signal,am and dm are approximation and detail coefficients at level m. These features are components of the feature vector F vi of the i-th signature. The feature vector is presented to the input of the signature verifier.
4. NEURAL NETWORK SIGNATURE VERIFIER The neural network models are commonly used for processing classification problems. But signature verification differs from the general classification problem. The goal of the general classification problem is to choose one class from several classes, whereas the training data contain data from all classes. For our application all the training data are authentic signatures and we have no data for the second class fake signatures. This is the reason why the frequently used supervised learned neural network model such as multi-layer perceptron cannot be applied to the signature verification task. 4.1. Architecture of the neural network verifier The adaptive resonance theory (ART), developed by Carpenter and Grossberg, was designed for clustering binary input vectors (ART-1) or continuous-valued input vectors (ART-2). With regards to the features what we used for description of signals, the ART-2 model is suitable for signature verification. The general architecture and description of the ART-2 network is not discussed here, for details see [3], [4]. The basic structure of the network verifier is illustrated in Figure 2. The network consists of two layers of processing elements labelled F1 (input and interface units) and F2 (cluster units), each fully interconnected with the others, and supplemental unit G and R (called gain control unit and reset unit), which are used to control the processing of the input data vector and creating of the clusters.
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The input and interface layer F1 has the same number of processing units as is the size of the feature vector (19 in our application). The clustering layer F2 consists of two processing units only, the former (labelled A) is active only if the feature vector corresponding to the authentic signature appears at the input of the network, the latter (labelled F) is active in other cases. More clusters are not enabled in our application. 4.2. Training and verification As was mentioned above, only the data for the authentic signature are known. Moreover, the number of template signatures cannot be too high because the acquisition of a large training set, e.g. at a bank counter could be boring and unpleasant for the customer. Hence only 5 signatures were used for the training of the ART neural network in our application. For these signatures corresponding feature vectors were evaluated and repeatedly presented to the input layer of the network (the slow learning mode was used for ART-2 network training). The parameters of the hidden sublayers of F1 and vigilance parameter ρ were set so that only the unit labelled A of layer F2 was active during the whole training procedure. When the training is completed, the network is prepared for verification. During the verification, only the vigilance parameter ρ have to be set properly to the authentic and the fake signatures were set to right clusters (ρ = 0.98 was used in our application). 5. EXPERIMENTAL RESULTS To test the verifier, signatures by 10 authors were taken. For each author, 20 authentic signatures and 15 fakes were recorded. The fake signatures were written by three different authors (5 fakes for each person). Sometimes the author is not satisfied with his/her own signature. The quality of signature depends on his/her physical and mental condition. In such a case the signatures can be classified as fakes. For the evaluation of such cases, the authors marked their authentic signatures by a mark from the scale 1-4 (1 means a best form of the signature). For verifier training, only the five signatures labelled by mark 1 or 2 were chosen. The summary of test results for 10 authors is presented in Table 1. 6. CONCLUSION AND FUTURE WORK The using of the special digital data acquisition pen and unsupervised learned ART-2 network for signature verification was discussed here. The tests showed that this network can be used as signature verifier and gives a
5 Table 1. Author No. 1 2 3 4 5 6 7 8 9 10
Results of verification tests
Authentic signatures classified as authentic fake 19 1 19 1 16 4 18 3 17 3 17 3 18 2 19 1 15 5 18 2
Fake Signatures classified as fake authentic 14 1 11 4 13 2 14 1 14 1 15 0 14 1 12 3 14 1 13 2
Overall Accuracy Ratio [%] 94.3 85.8 82.3 91.4 88.6 91.4 91.4 88.6 82.9 88.6
good result with respect to the training set size. Before the network training and the verification, only a small number of the network parameters had to be set manually. These parameters have remained at most the same in verification process too. In our future work, we plan to focus it on the setting of these parameters automatically during the training procedure as well as the automatic setting of the most important verification parameter - vigilance threshold. To improve the overall accuracy ratio, we plan to include the new valuable features to the feature vector describing the signature. Finally, we also plan to check the possibility of the application of other unsupervised learned neural network models (e.g. Kohonen self organizing feature map) for further improving of the signature verification task. Acknowledgments This work was supported by the Ministry of Education of the Czech Republic - project MSM 235200005 References 1. O.Rohlik et al., A New Approach to Signature Verification: Digital Data Acquisition Pen , Neural Network World, Vol. 11, No. 5, pp. 493-501,2001 2. S. Pitnerr and S.V. Kamarthi, Feature Extraction from Wavelet coefficients for Pattern Recognition Tasks, IEEE Transactions on PAMI, Vol. 21, No. 1, 1999 3. L. Fausett, Fundamentals of Neural Networks, Prentice-Hall, New Jersey, 1994. 4. G.A. Carpenter and S. Grossberg, ART-2: Self-organization of Stable Category Recognition Codes for Analog Input Patterns, Applied Optics, No. 26, pp. 4919-4930, 1987
