The algorithm of online handwritten signature verification based on improved DTW

Online handwritten signature verification is widely used because of its celerity and convenience. It is the personal verification based on biometrics technology. It is the verification using information about the movement during writing signature, so it becomes an accepted method of personal verification. Basing on this thesis, an online handwritten signature verification algorithm using improved DTW is presented. The weak point of classic DTW is that only the data points by warping the x-axis are considered but the differences on y-axis of two series are not considered. The two situations above are considered in the improved DTW method in order to prevent this problem and the distance measure of DTW is modified based on improved DTW in this paper particularly. The result of experiment proves that this algorithm obtains a good verification effect.

[1]  Ammar Soukkou,et al.  Multiobjective optimisation of robust Takagi-Sugeno fuzzy neural controller with hybrid learning algorithm , 2007, Int. J. Model. Identif. Control..

[2]  Changping Liu,et al.  On-line signature verification using local shape analysis , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[3]  Flávio Bortolozzi,et al.  Off-line signature verification using HMM for random, simple and skilled forgeries , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

[4]  Cemil Oz.  Signature Recognition and Verification with Artificial Neural Network Using Moment Invariant Method , 2005, ISNN.

[5]  Luan Fang-jun,et al.  On-Line Signature Handwritten Verification Algorithm Based on Stroke Matching , 2005 .

[6]  Yutaka Fukui,et al.  On-line signature verification method utilizing feature extraction based on DWT , 2003, Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03..

[7]  Eamonn J. Keogh,et al.  Derivative Dynamic Time Warping , 2001, SDM.

[8]  H. Y. Hwang,et al.  Online Signature Verification Based on Dynamic Feature Segmentation and 3-Step Matching , 2003, IDEAL.

[9]  Eamonn J. Keogh,et al.  Scaling up Dynamic Time Warping to Massive Dataset , 1999, PKDD.

[10]  Marek Kulbacki,et al.  Unsupervised Learning Motion Models Using Dynamic Time Warping , 2002, Intelligent Information Systems.

[11]  Sebastiano Impedovo,et al.  Analysis of stability in hand-written dynamic signatures , 2002, Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition.

[12]  Sharath Pankanti,et al.  Biometric Recognition: Security and Privacy Concerns , 2003, IEEE Secur. Priv..