Analysis of handwritten signature performances using mobile devices

This paper continue our previous work on on-line signature verification on portable devices. A database has been collected which contains data for 25 users, with 28 genuine and 25 skilled forged signatures per user, in 5 different devices. 4 portable devices of different sizes and screen technologies (capacitive and resistive) have been used. The 5th device is a traditional digital pen tablet, to use as a baseline for results comparison. Two different algorithms, DTW and SVM, have been used to asses the performance of the signals captured on portable devices. Two main experiments using both algorithms and the 5 sub-databases have been done. The first experiment uses each database independently. The user model is created and tested with signatures of the same database. The second experiment creates the user model using signatures acquired with the digital pen tablet. Results of the first experiment achieve good error rates for random forgeries.

[1]  D. Z. Lejtman,et al.  On-line handwritten signature verification using wavelets and back-propagation neural networks , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

[2]  Raul Sanchez-Reillo,et al.  Analysis on the resolution of the different signals in an on-line handwritten signature verification system applied to portable devices , 2010, 44th Annual 2010 IEEE International Carnahan Conference on Security Technology.

[3]  Oscar Miguel-Hurtado,et al.  On-line Signature Biometrics using Support Vector Machine , 2009, BIOSIG.

[4]  J. Liu-Jimenez,et al.  On-Line Signature Verification by Dynamic Time Warping and Gaussian Mixture Models , 2007, 2007 41st Annual IEEE International Carnahan Conference on Security Technology.

[5]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[6]  J. Simon,et al.  From Pixels to Features III: Frontiers in Handwriting Recognition , 1992 .

[7]  Julian Fiérrez,et al.  Feature Selection Based on Genetic Algorithms for On-Line Signature Verification , 2007, 2007 IEEE Workshop on Automatic Identification Advanced Technologies.

[8]  L. Yang,et al.  Application of hidden Markov models for signature verification , 1995, Pattern Recognit..