A segmentation-free biometric writer verification method based on continuous dynamic programming

By introducing the continuous dynamic programming (CDP) algorithm developed by Oka [Oka, R., 1998. Spotting method for classification of real world data. The Comput. J. 41 (8), 559–565], we have developed a new segmentation-free, text-independent biometric writer verification method 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 [Yamazaki, Y., Komatsu, N., 1999. Extraction of personal features from stroke shape, writing pressure and pen inclination in ordinary characters. In: Proc. 5th Internat. Conf. on Document Analysis and Recognition (ICDAR1999), pp. 426–429] based on a P-type Fourier descriptor-based learning vector quantisation scheme. To implement a text-independent, DP-based biometric writer verification, our 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. We 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.