Online signature verification with support vector machines based on LCSS kernel functions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Detection of alcohol intoxication via online handwritten signature verification
Pattern Recognition Letters
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In this paper, we refer a phenomenon of relatively high error rates against random forgeries in online signature verification. Though human can easily distinguish random forgeries from genuine signatures, the most published research results during last decades have reported high error rates against them, compared to the error rates against skilled forgeries. This paper suggests taking DPW (dynamic positional warping) approach for the problem, which supplies high accuracy and robustness against affine transform of online shapes. In addition, the effects of distance normalization and slope constraints are tested to DPW and conventional DTW (dynamic time warping). Our approach was compared to conventional DTW method with consequential xy difference information with SVC2004 task1 database: 0.17% and 1.47% of equal error rates are reported against random and skilled forgeries, respectively. Comparing to the best result of the conventional approach, the proposed approach reduced the error rates into about 27% in a single procedure without much additional time costs.