ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Strategies and Classification Methods for Off-Line Signature Verification
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Handwriting Analysis for Writer Verification
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Artificial Intelligence in Medicine
Multi-modal authentication using continuous dynamic programming
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
Text-independent writer recognition using multi-script handwritten texts
Pattern Recognition Letters
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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.