Verification by Using an Arbitrary Part of Feature Sequence Extracted from On-Line Handwritten Character / Figure Patterns

Writer Verification by Using an Arbitrary Part of Feature Sequence Extracted from On-Line Handwritten Character/Figure Patterns Hiroshi Kameya Chair of Supervisory Committee: Professor Ryuichi Oka Graduate Department of Information Systems By introducing the continuous dynamic programming (CDP) algorithm developed by Oka (1998), I have developed a new segmentation-free, text-independent biometric handwriting-based verification method (Kameya et al., 2006) improving the average correct acceptance rate and the average correct rejection rate of forgeries to a practically useful level of about 95% and 100%, respectively, outperforming the only other biometric text-independent writer verification method of Yamazaki and Komatsu (1996a,b, 1999, 2000) based on a P-type Fourier descriptor-based learning vector quantisation scheme. To implement a text-independent, DP-based biometric writer verification, my spotting-enabling CDP algorithm has not only successfully traced and tracked an optimal microscopic dynamic features of real-time writing processes of either scanned characters or images of relevant individuals but also has implemented extension to a more secure text-independent writer verification method by exploiting the built-in spotting function of the CDP method, which is capable of ignoring inputs outside the task domain. This is so because a text-independent sample can be selected from any of many possible candidates located at an arbitrary location within the specified long reference sample. I have demonstrated the applicability of the method not only to Japanese text but also to English text that tends to assess the language independence of the method.