Research on the Rejection Capabilities of the Signature-pressure-based Individual Recognition System for Counterfeit Signatures Using Optimized Neuro-template with Gaussian Function

In recent years, internet business has been developing rapidly along with the wide application of Internet and the security of users’ information has become more and more important point for the development of internet business. Therefore developing an individual recognition system especially fit for internet application is becoming pressing task. Recently, biometrics information, such as finger print, iris, voice, face and signature, has been increasingly adopted in personal identification because of being unique and having high resistance to forgery. In this research, a novel individual recognition system, in which biometrics information of signature pressure is exclusively employed to present personal feature, is developed for the application of internet business. The execution of the system includes two procedures, registration and recognition. First the user give three register signatures to register on the system (registration), after registration, the user can log on the system by giving one test signature (recognition) at anytime. In both procedures, the signature pressure data will be preprocessed firstly and then the data obtained from preprocessing of source pressure data are used either for registration or for recognition. Therefore two parts, preprocessing and neural network classifier (NN classifier), are included in the structure of the system. In the preprocessing, the detected signature pressure data is firstly normalized, then equally dividing the normalized data into 300 sections and average value of each section is calculated as element of relay data. Second, validity check is executed on three relay data of register signatures. Last, the probability distributions of register relay data and inhibit relay data, which are artificially made by system, are analyzed and 50 elements are extracted as slab value from each relay data, which are used for NN learning or input to NN. In the preprocessing, the scale of source data is greatly reduced and personal feature of signature pressure are also extracted. The neural network classifier of system is mainly studied in this paper and the uniqueness works in the research of this paper mainly include the following points. 1) Neuro-template Matching Method is introduced into the NN classifier of the system. According to this method, each registrant is assigned with a three-layer feed-forward neural network with uniform structure of 50×35×2 and the NN classifier of the system is composed with the neuro-templates of all registrants. In case of registration, after learning with preprocessed source data of register signatures as 削除: ,