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Online Signature Verification Based On Multi-dimensional Force Information

Posted on:2009-01-30Degree:DoctorType:Dissertation
Country:ChinaCandidate:D H ZhangFull Text:PDF
GTID:1118360272462513Subject:Pattern Recognition and Intelligent Systems
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With the development of society,more and more business activities and work practices are computerized.Personal identification and verification play a critical role in our society.Ecommerce applications,such as e-banking,or security applications, such as building access,demand real-time and accurate personal identification. Traditional knowledge-based or token-based personal identification or verification systems are time-consuming,inconvenient to use,easy to be forgotten,expensive and easy to be imitated or copied.Biometrics refers to automatic recognition of people based on their distinctive anatomical(e.g.,face,fingerprint,iris,etc.) and behavioral (e.g.,online/off-line signature,voice,gait,etc.) characteristics.Biometrics based authentication can overcome some of the limitations of the traditional automatic personal identification technologies,but still,new algorithms and solutions are required to improve the performance.Compared with other personal verification methods,the handwritten signature is a well-accepted one in our daily life.Signature verification can be divided into two categories:online signature verification and offline signature verification.Since the former can make full use of both the static features and the dynamic features of a handwritten signature,it can obtain better verification results in practice.The work included in this dissertation focuses on the online signatures verification methods and their applications described as follows:(1) A platform for signature capturing and processing was built based on a F_Tablet which can not only capture the shape series(x,y) but also the three-dimensional force series(Fx,Fy,Fz),and a signature database was also constructed by using the F_Tablet.Meanwhile the studies on various signature verification algorithms were carried out.(2) 188 global features were extracted from the signature according to the multi-dimensional forces.These features included force features as well as shape features.The weight function of features was then defined and used to sort the features extracted and the personalized features that can help separate the genuine signatures from the fake ones were selected.Then the support vector machine based method and hidden Markov model based method were used to verify the features selected,and the corresponding results were compared with the results based on principal component analysis and linear discriminant analysis.The experimental results showed that the weight function of features had good effect.Moreover,five key global features were selected from further observation of all the global features of signatures.The experimental results showed that simple forgery could be easily and quickly detected by using the above five key global features.(3) The signature was divided into segments by the method that synthesized the valley point of the pressure and the local minimum point of the velocity.Segment features were extracted from every segment.We verified the signatures using methods based on hidden Markov model and based on string matching respectively.The results showed that the segment features could effectively represent the differences between the genuine and fake signatures.(4) An improved dynamic time warping algorithm was proposed to verify the force series and the shape series of the signatures.Compared to the usual dynamic time warping,the improved algorithm got better result.Resampled the force series and the shape series and then verified the signatures using the algorithm based on principal component analysis.(5) Finally,a multi-feature based online signature verification method was proposed.This method synthesized the global features,the segment features,the force series and the shape series.The multi-feature based decision fusion method proved to have better result.
Keywords/Search Tags:signature verification, weight of features, support vector machine, dynamic time warping, principal component analysis, linear discriminant analysis, hidden Markov model, feature fusion, biometrics
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