Text-Independent writer identification based on fusion of dynamic and static features

Authors:
Wenfeng Jin;Yunhong Wang;Tieniu Tan
Affiliations:
Center for Biometrics and Security Research (CBSR), National Lab of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, Beijing, P. R. China;School of Computer Science and Engineering, Beihang University, Beijing, P. R. China;Center for Biometrics and Security Research (CBSR), National Lab of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, Beijing, P. R. China
Venue:
IWBRS'05 Proceedings of the 2005 international conference on Advances in Biometric Person Authentication
Year:
2005

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Pattern Recognition

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Abstract

Handwriting recognition is a traditional and natural approach for personal authentication. Compared to signature verification, text-independent writer identification has gained more attention for its advantage of denying imposters in recent years. Dynamic features and static features of the handwriting are usually adopted for writer identification separately. For text-independent writer identification, by using a single classifier with the dynamic or the static feature, the accuracy is low, and many characters are required (more than 150 characters on average). In this paper, we developed a writer identification method to combine the matching results of two classifiers which employs the static feature (texture) and dynamic features individually. Sum-Rule, Common Weighted Sum-Rule and User-specific Sum-Rule are applied as the fusion strategy. Especially, we gave an improvement for the user-specific Sum-Rule algorithm by using an error-score. Experiments were conducted on the NLPR handwriting database involving 55 persons. The results show that the combination methods can improve the identification accuracy and reduce the number of characters required.