Effective information extraction and optimal feature subspace selection have great effects on the performance of online signature verification. One of the difficulties about online signature verification is to extract and select effective features, which are stable in genuine but discriminative to distinguish forgeries. Different from other works, stability of spectral information inherent in signatures is analyzed in this paper, and stable spectral information is selected dynamically dependent on individuals to reconstruct the stable spectral features. In order to extract more effective spectral information, features are decomposed by wavelet packet with the optimal mother wavelet. To enhance the security level of online signature verification, discriminative capabilities of stable spectral features are analyzed by factorial experiment design. The optimal feature subspace is selected according to contribution rate. Furthermore, we proposed a simple and effective modified dynamic time warping (DTW) with signature curves constraint to solve the problem of heavy computation of DTW. Several experiments are carried out on open access database of MCYT_DB1 and SVC2004 task2 which consist of 6600 signatures from 140 individuals in total. Experiment results demonstrate the effectiveness and robustness of our proposed method.

Online Signature Verification Based on Stable Features Extracted Dynamically

online or Dynamic signature verification (DSV) is one of the most acceptable, intuitive, fast and cost effective tool for user authentication. DSV uses some dynamics like speed, pressure, directions, stroke length and pen-ups/pen-downs to verify the signer's identity. The state of the art in DSV is presented in this paper. several approaches for DSV are compared and the most influential techniques in this field are highlighted. We concentrate on the relationship between the verification approach used (the nature of the classifier) and the type of features that are used to represent the signature.

Online Signature Verification: State of the art

Consistency analysis of signature features has significant effect on the performance and robustness of on- line signature verification. Inorder to improve the performance of on-line signature verification, we propose a method of on-line signature verification based on spectral consistent features, in which the consistent and effective information is extracted of signature features to be used in verification. In our works, spectral consistency of features is analyzed by statistics along with the consistent penalty factor. The consistent spectral features are obtained dynamically by the wavelet packet reconstruction with consistent sub-bands. Similarity between test signature and reference is calculated by proposed CPF-DTW to improve the robustness. Several experiments are carried out both on CDB and DB1 which consists of 8150 signatures from 190 individuals in total, the best results of our proposed method are given by EERCDB=2.25% and EERDB1=2.38% respectively which indicate the effectiveness and robustness of our proposed methods. Keywords-on-line signature verification; consistent spectral features; consistency analysis; wavelet packet; CPF-DTW

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A Method of On-Line Signature Verification with Consistent Spectral Features

Drawing is an important fine motor activity, and school-age children are at an ideal age for developing fine motor skills. This study investigated the development of primary school children's kinematic skills in drawing using a three-dimensional force-sensitive tablet. From a primary school, Sixty children with typical handwriting processes were asked to complete drawing tasks on a digital tablet for kinematic data collection. Four types of shapes including horizontal lines, vertical lines, squares and circles, were used in drawing tasks. We studied a series of kinematic parameters, such as velocity, acceleration and three-dimensional handwriting force vector information. Results show that velocity and acceleration increased with the grade level for four types of tasks, while the mean force of the z-axis decreased in all tasks. Specifically, the writing force of the x-axis decreased significantly in the horizontal line drawing task, and the writing force of the y-axis decreased significantly in the vertical line drawing task. The parameters extracted and observed in this study are useful for studying the development and acquisition of drawing skills. These values can be used as references points to evaluate samples with different characteristics.

Kinematic Analysis of Drawing Movements in Chinese Primary Schoolchildren

On-line signature verification is one of widely acceptable biometrics. Since it can reveal more intrinsic characteristics inherent in writer, spectral information that contains in features can be used to on-line signature verification. In order to extract more effective spectral information, features are decomposed by Wavelet packet (WP) with optimal mother wavelet. Information entropy theory is introduced to reveal the disorder of spectral information contained in signature. The optimal mother wavelet, which is used in WP analysis, is selection based on maximum energy-entropy values to extract more effective information of signature to be used in verification. Because of different writing habits, stabilities of spectral information inherent in signatures maybe varied. To improve the robustness only stable spectral information is extracted. Several experiments are carried out on standard on-line signature dataset MCYT_Subcorpus_100 (DB1), which consists of 5000 signatures from 100 individuals in total. Experiment results demonstrate that the efficiency of on-line signature verification is improved greatly by our proposed method. The best result is given by EER=3.11%, which also indicates the effectiveness of our proposed methods.

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Optimal Mother Wavelet Selection for On-line Signature Verification

On-line signature verification is one of the most accepted means for personal verification. This paper proposes an on-line signature verification method based on Wavelet Transform (WT). Firstly, the method uses wavelet transform to exact characteristic points of 3-axis force and 2-dimension coordinate of signature obtained by the F-Tablet. And then it builds 5-dimension feature sequences and dynamically creates multi-templates using clustering. Finally, after the fusion of the above-mentioned 5-dimension feature sequences, whether the signature is genuine or not is decided by majority voting scheme. Experimenting on a signature database acquired by F-Tablet, the performance evaluation in even EER (Equal Error Rate) was improved to 2.83%. The experimental results show that the method not only reduces the amount of data to be stored, but also minimizes the duration of the whole authentication processing and increases the efficiency of signature verification.

ZhongCheng Wu

Papers

On-line signature verification based on wavelet transform to extract characteristic points